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In the research of dynamic MRI imaging, we have utilized the minimax-concave (MC) function to replace the traditional nuclear norm, thereby significantly enhancing the model's fitting accuracy. The Total Variation (TV) duality technique is introduced to refine the model resolution further, better capturing underlying structures in MRI data. The splitting method is also employed to establish a comprehensive solution framework for the model. A high-efficiency algorithm, designed using the proximity operator, is developed to solve sub-problems within this framework, ensuring swift and accurate convergence. Numerical experiments on various dynamic MRI datasets and undersampling templates demonstrate that the new method outperforms traditional approaches in several key metrics, including CPU runtime, iteration count, and reconstruction performance. The new method reduces computational overhead, expedites the image reconstruction process, and significantly improves the quality of reconstructed images. [ABSTRACT FROM AUTHOR]

Background: In-situ micro-computed tomography (µCT) technology is an attractive approach to investigate the evolution process of damage inside ceramic matrix composites (CMCs) during high-temperature service. The evolution process is highly time-sensitive under temperature-induced loads, and fast scanning is very necessary for in-situ µCT tests. Objective: The objective of this work is to provide a fast scanning method for in situ µCT tests on CMCs with complex microstructures by the innovation of a reconstruction algorithm. Method: To overcome the severe degradation of the reconstructed image quality resulting from sparse CT scans, a deep-learning-based multi-domain sparse reconstruction method was proposed. Three sub-networks including the projection-domain, image-domain, and fusion network were constructed in the multi-domain method to make full use of the information from the projection and image domain. Results: The proposed deep-learning-based sparse reconstruction method provided satisfactory µCT images on C/SiC composites with acceptable quality. The scanning time was reduced by 6 times. All selected evaluation metrics of the proposed method are higher than those of other single-domain methods and traditional iterative method. The segmentation accuracy of the µCT images obtained by the proposed method can meet the subsequent quantitative analysis. An in-situ tensile test of CMCs is conducted to further evaluate the performance in the practical application of in-situ experiments. The results indicate that the weak and thin micro-cracks can still be effectively retained and recovered. A detailed workflow to implement the method generally is also provided. Conclusions: Based on the deep-learning-based multi-domain sparse reconstruction method, the process of in-situ µCT tests can be greatly accelerated with little loss of the reconstructed image quality. [ABSTRACT FROM AUTHOR]

With the development of image feature matching technology, feature matching algorithms based on deep learning have achieved excellent results, but in scenarios with low texture or extreme perspective changes, the matching accuracy is still difficult to guarantee. In this paper, a superresolution reconstruction method based on a Residual-ESPCN (efficient subpixel convolutional neural network) approach is proposed based on LoFTR (local feature matching with transformers). The superresolution method is used to improve the interpolation method used in ASFF (adaptive spatial feature fusion) to increase the image resolution, enhance the detailed information of the image, and make the extracted features richer. Then, ASFF is introduced into the local feature extraction module of LoFTR, which can alleviate the inconsistency problem of information transmission between different scale features of the feature pyramid and lessen the amount of information lost during transmission from low- to high-resolution levels. Moreover, to improve the adaptability of the algorithm to different scenarios, OTSU is introduced to adaptively calculate the threshold of feature matching. The experimental results show that in different indoor or outdoor scenarios, our proposed algorithm for matching features can effectively improve the adaptability of feature matching and can achieve good results in terms of the area under the curve (AUC), accuracy and recall. [ABSTRACT FROM AUTHOR]

Aiming at the problems of traditional image super-resolution reconstruction algorithms in the image reconstruction process, such as small receptive field, insufficient multi-scale feature extraction, and easy loss of image feature information, a super-resolution reconstruction algorithm of multi-scale dilated convolution network based on dilated convolution is proposed in this paper. First, the algorithm extracts features from the same input image through the dilated convolution kernels of different receptive fields to obtain feature maps with different scales; then, through the residual attention dense block, further obtain the features of the original low resolution images, local residual connections are added to fuse multi-scale feature information between multiple channels, and residual nested networks and jump connections are used at the same time to speed up deep network convergence and avoid network degradation problems. Finally, deep network extraction features, and it is fused with input features to increase the nonlinear expression ability of the network to enhance the superresolution reconstruction effect. Experimental results show that compared with Bicubic, SRCNN, ESPCN, VDSR, DRCN, LapSRN, MemNet, and DSRNet algorithms on the Set5, Set14, BSDS100, and Urban100 test sets, the proposed algorithm has improved peak signal-to-noise ratio and structural similarity, and reconstructed images. The visual effect is better. [ABSTRACT FROM AUTHOR]

Three-dimensional (3D) image reconstruction techniques have found extensive applications in fields such as medicine, education, and computer science, enabling high-precision 3D images to enhance work efficiency. However, traditional methods of 3D image reconstruction solely rely on acoustic information, resulting in limited accuracy. Therefore, a novel method based on virtual reality (VR) and multimodal deep learning is proposed for 3D image reconstruction. First, VR technology is employed to capture 3D image information, followed by de-noising and removal of redundant information. Second, a logarithmic transformation method is employed to enhance the details in the 3D image. Finally, a multimodal deep learning method is utilized to reconstruct the 3D image from the perspectives of imagery, sound, and video. Experimental results demonstrate that the proposed method achieves superior 3D image reconstruction with an accuracy of over 90%. The reconstruction process is efficient and exhibits low signal-to-noise ratio, while the average registration error is less than 0.04%. These findings highlight the practical value and potential applications of the proposed method. [ABSTRACT FROM AUTHOR]

Image super‐resolution (SR) reconstruction is a key technique for improving image quality and details. Conventional methods are frequently limited by interpolation, filtering, or statistical approaches; thus, they are unable to reconstruct high‐quality continuously enlarged images with detailed information. This study proposes an image SR reconstruction network model, called LALNet, based on implicit image functions and residual multilayered perceptron (RAMLP) with an attention mechanism. Through the implicit image function and RAMLP + attention, high‐quality SR reconstruction with continuous scale factors is achieved, and LALNets can run on embedded edge computing platforms. This method exhibits the following advantages: lightweight network structure reduces computing requirements, introduction of implicit image functions and RAMLP improves reconstruction quality, and attention mechanism suppresses artefacts and distortions. Experimental results show that LALNet outperforms traditional and other deep learning methods in terms of reconstruction performance and computational efficiency. This research provides new ideas and methods for the further development of the field of image SR reconstruction. [ABSTRACT FROM AUTHOR]

Computed tomography (CT) imaging plays a crucial role in various medical applications, but noise in projection data can significantly degrade image quality and hinder diagnosis accuracy. Iterative algorithms for tomographic image reconstruction outperform transform methods, especially in scenarios with severe noise in projections. In this paper, we propose a method to dynamically adjust two parameters included in the iterative rules during the reconstruction process. The algorithm, named the parameter-extended expectation-maximization based on power divergence (PXEM), aims to minimize the weighted extended power divergence between the measured and forward projections at each iteration. Our numerical and physical experiments showed that PXEM surpassed conventional methods such as maximum-likelihood expectation-maximization (MLEM), particularly in noisy scenarios. PXEM combines the noise suppression capabilities of power divergence-based expectation-maximization with static parameters at every iteration and the edge preservation properties of MLEM. The experimental results demonstrated significant improvements in image quality in metrics such as the structural similarity index measure and peak signal-to-noise ratio. PXEM improves CT image reconstruction quality under high noise conditions through enhanced optimization techniques. [ABSTRACT FROM AUTHOR]

In this paper, we present a novel sparse Bayesian learning (SBL) method for image reconstruction. We integrate the generalized approximate message passing (GAMP) algorithm and Laplacian hierarchical priors (LHP) into a basic SBL model (called LHP-GAMP-SBL) to improve the reconstruction efficiency. In our SBL model, the GAMP structure is used to estimate the mean and variance without matrix inversion in the E-step, while LHP is used to update the hyperparameters in the M-step.The combination of these two structures further deepens the hierarchical structures of the model. The representation ability of the model is enhanced so that the reconstruction accuracy can be improved. Moreover, the introduction of LHP accelerates the convergence of GAMP, which shortens the reconstruction time of the model. Experimental results verify the effectiveness of our method. [ABSTRACT FROM AUTHOR]

Objectives: Magnetic resonance imaging (MRI) is a commonly used tool in clinical medicine, but it suffers from the disadvantage of slow imaging speed. To address this, we propose a novel MRI reconstruction algorithm based on image decomposition to realize accurate image reconstruction with undersampled k‐space data. Methods: In our algorithm, the MR images to be recovered are split into cartoon and texture components utilizing image decomposition theory. Different sparse transform constraints are applied to each component based on their morphological structure characteristics. The total variation transform constraint is used for the smooth cartoon component, while the L0 norm constraint of tight frame redundant transform is used for the oscillatory texture component. Finally, an alternating iterative minimization approach is adopted to complete the reconstruction. Results: Numerous numerical experiments are conducted on several MR images and the results consistently show that, compared with the existing classical compressed sensing algorithm, our algorithm significantly improves the peak signal‐to‐noise ratio of the reconstructed images and preserves more image details. Conclusions: Our algorithm harnesses the sparse characteristics of different image components to reconstruct MR images accurately with highly undersampled data. It can greatly accelerate MRI speed and be extended to other imaging reconstruction fields. [ABSTRACT FROM AUTHOR]

Pulmonary monitoring is crucial for the diagnosis and management of respiratory conditions, especially after the epidemic of coronavirus disease. Electrical impedance tomography (EIT) is an alternative non-radioactive tomographic imaging tool for monitoring pulmonary conditions. This review proffers the current EIT technical principles and applications on pulmonary monitoring, which gives a comprehensive summary of EIT applied on the chest and encourages its extensive usage to clinical physicians. The technical principles involving EIT instrumentations and image reconstruction algorithms are explained in detail, and the conditional selection is recommended based on clinical application scenarios. For applications, specifically, the monitoring of ventilation/perfusion (V/Q) is one of the most developed EIT applications. The matching correlation of V/Q could indicate many pulmonary diseases, e.g., the acute respiratory distress syndrome, pneumothorax, pulmonary embolism, and pulmonary edema. Several recently emerging applications like lung transplantation are also briefly introduced as supplementary applications that have potential and are about to be developed in the future. In addition, the limitations, disadvantages, and developing trends of EIT are discussed, indicating that EIT will still be in a long-term development stage before large-scale clinical applications. [ABSTRACT FROM AUTHOR]

Metal artifacts notoriously pose significant challenge in computed tomography (CT), leading to inaccuracies in image formation and interpretation. Artifact reduction tools have been designed to improve cone beam computed tomography (CBCT) image quality by reducing artifacts caused by certain high-density materials. Metal artifact reduction (MAR) tools are specific algorithms that are applied during image reconstruction to minimize or eliminate artifacts degrading CBCT images. The purpose of the study is to evaluate the effect of a MAR algorithm on image quality in CBCT performed for evaluating patients before transarterial radioembolization (TARE). We retrospectively included 40 consecutive patients (aged 65 ± 13 years; 23 males) who underwent 45 CBCT examinations (Allura FD 20, XperCT Roll protocol, Philips Healthcare, Best, The Netherlands) in the setting of evaluation for TARE between January 2017 and December 2018. Artifacts caused by coils, catheters, and surgical clips were scored subjectively by four readers on a 5-point scale (1 = artifacts affecting diagnostic information to 5 = no artifacts) using a side-by-side display of uncorrected and MAR-corrected images. In addition, readers scored tumor visibility and vessel discrimination. MAR-corrected images were assigned higher scores, indicating better image quality. The differences between the measurements with and without MAR were most impressive for coils with a mean improvement of 1.6 points (95%CI [1.5 1.8]) on the 5-point likert scale, followed by catheters 1.4 points (95%CI [1.3 1.5]) and clips 0.7 points (95%CI [0.3 1.1]). Improvements for other artifact sources were consistent but relatively small (below 0.25 points on average). Interrater agreement was good to perfect (Kendall's W coefficient = 0.68–0.95) and was higher for MAR-corrected images, indicating that MAR improves diagnostic accuracy. A metal artifact reduction algorithm can improve diagnostic and interventional accuracy of cone beam CT in patients undergoing radioembolization by reducing artifacts caused by diagnostic catheters and coils, lowering interference of metal artifacts with adjacent major structures, and improving tumor visibility. [ABSTRACT FROM AUTHOR]

Positron Emission Tomography (PET) image reconstruction remains a pivotal area in PET technology, critically influencing clinical diagnostic outcomes. Addressing the need for enhanced image quality, this study introduces a novel algorithm for PET image reconstruction. This algorithm integrates a penalty mechanism, morphological filtering, and the Maximum Likelihood Expectation Maximization (MLEM) algorithm, aiming to improve reconstructed image quality significantly. The operational process of the algorithm within each iteration encompasses two primary phases. Initially, image reconstruction is accomplished via the MLEM algorithm, followed by the application of a morphological filter to attenuate noise in the reconstructed image. Simulation experiments demonstrate that this algorithm effectively mitigates noise while preserving crucial details, such as image edges. Notably, this method presents the dual benefits of straightforward parameter configuration and ease of implementation. The results indicate a substantial enhancement in noise suppression and fine structure preservation in the reconstructed images, marking a significant advancement in PET image reconstruction techniques. [ABSTRACT FROM AUTHOR]

Ultrasound imaging has a history of several decades. With its non-invasive, low-cost advantages, this technology has been widely used in medicine and there have been many significant breakthroughs in ultrasound imaging. Even so, there are still some drawbacks. Therefore, some novel image reconstruction and image analysis algorithms have been proposed to solve these problems. Although these new solutions have some effects, many of them introduce some other side effects, such as high computational complexity in beamforming. At the same time, the usage requirements of medical ultrasound equipment are relatively high, and it is not very user- friendly for inexperienced beginners. As artificial intelligence technology advances, some researchers have initiated efforts to deploy deep learning to address challenges in ultrasound imaging, such as reducing computational complexity in adaptive beamforming and aiding novices in image acquisition. In this survey, we are about to explore the application of deep learning in medical ultrasound imaging, spanning from image reconstruction to clinical diagnosis. [ABSTRACT FROM AUTHOR]

Wavefront Coding (WFC) is an innovative technique aimed at extending the depth of focus (DOF) of optics imaging systems. In digital imaging systems, super-resolution digital reconstruction close to the diffraction limit of optical systems has always been a hot research topic. With the design of a point spread function (PSF) generated by a suitably phase mask, WFC could also be used in super-resolution image reconstruction. In this paper, we use a deep learning network combined with WFC as a general framework for images reconstruction, and verify its possibility and effectiveness. Considering the blur and additive noise simultaneously, we proposed three super-resolution image reconstruction procedures utilizing convolutional neural networks (CNN) based on mean square error (MSE) loss, conditional Generative Adversarial Networks (CGAN), and Swin Transformer Networks (SwinIR) based on mean absolute error (MAE) loss. We verified their effectiveness by simulation experiments. A comparison of experimental results shows that the SwinIR deep residual network structure based on MAE loss optimization criteria can generate more realistic super-resolution images with more details. In addition, we used a WFC camera to obtain a resolution test target and real scene images for experiments. Using the resolution test target, we demonstrated that the spatial resolution could be improved from 55.6 lp/mm to 124 lp/mm by the proposed super-resolution reconstruction procedure. The reconstruction results show that the proposed deep learning network model is superior to the traditional method in reconstructing high-frequency details and effectively suppressing noise, with the resolution approaching the diffraction limit. [ABSTRACT FROM AUTHOR]

Background: Multiplexed positron emission tomography (mPET) imaging can measure physiological and pathological information from different tracers simultaneously in a single scan. Separation of the multiplexed PET signals within a single PET scan is challenging due to the fact that each tracer gives rise to indistinguishable 511 keV photon pairs, and thus no unique energy information for differentiating the source of each photon pair. Methods: Recently, many applications of deep learning for mPET image separation have been concentrated on pure data-driven methods, e.g., training a neural network to separate mPET images into single-tracer dynamic/static images. These methods use over-parameterized networks with only a very weak inductive prior. In this work, we improve the inductive prior of the deep network by incorporating a general kinetic model based on spectral analysis. The model is incorporated, along with deep networks, into an unrolled image-space version of an iterative fully 4D PET reconstruction algorithm. Results: The performance of the proposed method was evaluated on a simulated brain image dataset for dual-tracer [ 18 F]FDG+[ 11 C]MET PET image separation. The results demonstrate that the proposed method can achieve separation performance comparable to that obtained with single-tracer imaging. In addition, the proposed method outperformed the model-based separation methods (the conventional voxel-wise multi-tracer compartment modeling method (v-MTCM) and the image-space dual-tracer version of the fully 4D PET image reconstruction algorithm (IS-F4D)), as well as a pure data-driven separation [using a convolutional encoder-decoder (CED)], with fewer training examples. Conclusions: This work proposes a kinetic model-informed unrolled deep learning method for mPET image separation. In simulation studies, the method proved able to outperform both the conventional v-MTCM method and a pure data-driven CED with less training data. [ABSTRACT FROM AUTHOR]

Aiming at solving the problem that the capability of direction of arrival method for estimation of nested arrays decreases sharply under unknown mutual coupling, an iterative weighted low‐rank matrix reconstruction algorithm is proposed here. First, the received data covariance matrix is extended, and a weighted low‐rank matrix recovery problem is formulated for joint estimations of the Toeplitz matrix and mutual coupling coefficients. Next, the direction of arrival from recovered Toeplitz matrix is retrieved by using the root multiple signal classification algorithm. Simulation results show that iterative weighted low‐rank matrix reconstruction algorithm can effectively avoid the influence of mutual coupling while solving the grid‐mismatch problem so that the accuracy and the resolution of estimation are improved. [ABSTRACT FROM AUTHOR]

Removing texture while preserving the main structure of an image is a challenging task. To address this, this paper propose an image smoothing method based on global gradient sparsity and local relative gradient constraints optimization. To reduce the interference of complex texture details, adopting a multi-directional difference constrained global gradient sparsity decomposition method, which provides a guidance image with weaker texture detail gradients. Meanwhile, using the luminance channel as a reference, edge-aware operator is constructed based on local gradient constraints. This operator weakens the gradients of repetitive and similar texture details, enabling it to obtain more accurate structural information for guiding global optimization of the image. By projecting multi-directional differences onto the horizontal and vertical directions, a mapping from multi-directional differences to bi-directional gradients is achieved. Additionally, to ensure the consistency of measurement results, a multi-directional gradient normalization method is designed. Through experiments, we demonstrate that our method exhibits significant advantages in preserving image edges compared to current advanced smoothing methods. [ABSTRACT FROM AUTHOR]

We present the first machine learning-based autonomous hyperspectral neutron computed tomography experiment performed at the Spallation Neutron Source. Hyperspectral neutron computed tomography allows the characterization of samples by enabling the reconstruction of crystallographic information and elemental/isotopic composition of objects relevant to materials science. High quality reconstructions using traditional algorithms such as the filtered back projection require a high signal-to-noise ratio across a wide wavelength range combined with a large number of projections. This results in scan times of several days to acquire hundreds of hyperspectral projections, during which end users have minimal feedback. To address these challenges, a golden ratio scanning protocol combined with model-based image reconstruction algorithms have been proposed. This novel approach enables high quality real-time reconstructions from streaming experimental data, thus providing feedback to users, while requiring fewer yet a fixed number of projections compared to the filtered back projection method. In this paper, we propose a novel machine learning criterion that can terminate a streaming neutron tomography scan once sufficient information is obtained based on the current set of measurements. Our decision criterion uses a quality score which combines a reference-free image quality metric computed using a pre-trained deep neural network with a metric that measures differences between consecutive reconstructions. The results show that our method can reduce the measurement time by approximately a factor of five compared to a baseline method based on filtered back projection for the samples we studied while automatically terminating the scans. [ABSTRACT FROM AUTHOR]

The management and processing of synchrotron and neutron computed tomography data can be a complex, labor‐intensive and unstructured process. Users devote substantial time to both manually processing their data (i.e. organizing data/metadata, applying image filters etc.) and waiting for the computation of iterative alignment and reconstruction algorithms to finish. In this work, we present a solution to these problems: TomoPyUI, a user interface for the well known tomography data processing package TomoPy. This highly visual Python software package guides the user through the tomography processing pipeline from data import, preprocessing, alignment and finally to 3D volume reconstruction. The TomoPyUI systematic intermediate data and metadata storage system improves organization, and the inspection and manipulation tools (built within the application) help to avoid interrupted workflows. Notably, TomoPyUI operates entirely within a Jupyter environment. Herein, we provide a summary of these key features of TomoPyUI, along with an overview of the tomography processing pipeline, a discussion of the landscape of existing tomography processing software and the purpose of TomoPyUI, and a demonstration of its capabilities for real tomography data collected at SSRL beamline 6‐2c. [ABSTRACT FROM AUTHOR]

Blind compressive sensing generates a reduced set of measurements of a signal without the knowledge of the sparsifying basis. In this context, the unique recovery and reconstruction of the underlying signal is a challenging problem. One of the properties of the Householder operator is to transform a non-sparse vector in a finite dimensional vector space to a sparse representation of the vector, preserving its ℓ 2 -norm. In this work, we present an algorithm that invokes the sparsifying property of the Householder transforms for the recovery and reconstruction of a signal from its reduced set of blind measurements. The algorithm is an iterative procedure using two objective functions. The first objective function is to estimate the sparse set of representing coefficients. The second objective function jointly estimates the unknown sparsifying transform and the unknown signal. Both the objective functions are entangled to ensure that the estimates meet the fidelity requirement with the available reduced set of measurements. An ℓ 1 -trend filtering is applied to minimize overfitting the estimate of the signal. The signal is uniquely recovered and reconstructed upto an acceptable lower error bound. [ABSTRACT FROM AUTHOR]

Depth estimation tries to obtain 3D scene geometry from low-dimensional data like 2D images. This is a vital operation in computer vision and any general solution must preserve all depth information of potential relevance to support higher-level tasks. For scenes with well-defined depth, this work shows that multi-view edges can encode all relevant information—that multi-view edges are complete. For this, we follow Elder's complementary work on the completeness of 2D edges for image reconstruction. We deploy an image-space geometric representation: an encoding of multi-view scene edges as constraints and a diffusion reconstruction method for inverting this code into depth maps. Due to inaccurate constraints, diffusion-based methods have previously underperformed against deep learning methods; however, we will reassess the value of diffusion-based methods and show their competitiveness without requiring training data. To begin, we work with structured light fields and epipolar plane images (EPIs). EPIs present high-gradient edges in the angular domain: with correct processing, EPIs provide depth constraints with accurate occlusion boundaries and view consistency. Then, we present a differentiable representation form that allows the constraints and the diffusion reconstruction to be optimized in an unsupervised way via a multi-view reconstruction loss. This is based around point splatting via radiative transport, and extends to unstructured multi-view images. We evaluate our reconstructions for accuracy, occlusion handling, view consistency, and sparsity to show that they retain the geometric information required for higher-level tasks. [ABSTRACT FROM AUTHOR]

Background: High-quality images with minimum radiation dose are considered a challenge in Computed Tomography (CT) scans. Objective: The current study aimed to assess the efficacy of the Iterative Reconstruction in Image Space (IRIS) algorithm combined with Automatic Tube Current Modulation (ATCM) compared to Filtered Back Projection (FBP) in brain CT scans. Material and Methods: In this cross-sectional study, 200 patients underwent to brain CT scan, and images were then reconstructed using both FBP and IRIS. The CT Number (CTN), noise, and Signal-to-Noise Ratio (SNR) were computed for different tissues from CT images. The performance of two algorithms under different exposure conditions was evaluated using a water phantom. Two experienced radiologists assessed the image quality. Volume CT Dose Index (CTDIvol) and Dose Length Product (DLP) were recorded for each scan. Results: FBP reconstruction exhibited higher noise and lower SNR compared to IRIS, both with and without ATCM. Noise levels significantly increased for FBP combined with ATCM. Subjective analysis showed higher performance for IRIS without ATCM compared to other approaches. The mean CTDIvol with and without ATCM was 20.04±3.33 and 36.37±4.65 mGy, respectively. In the phantom study, the noise with IRIS remained lower than that with FBP even with a 42% dose reduction. Conclusion: IRIS algorithm can preserve the image quality when radiation dose is significantly reduced by ATCM in brain CT scan. Implementation of IRIS combined with ATCM is recommended for brain CT examinations. [ABSTRACT FROM AUTHOR]

Setting up a complex CNN requires a powerful platform, several hours of run-time, and a lot of data for training. Here, we propose a generalised lightweight solution that exploits super-resolution and scalable vector graphics and uses a small-scale UNet as the baseline framework to segment different organs in MR and CT data. We selected the UNet since many researchers use it as the baseline, modify it in their proposal, and perform an ablation study to show the effectiveness of the proposed modification. First, we downsample the input 2D CT slices by bicubic interpolation. Using the architecture of the conventional UNet, we reduce the size of the network's input, and the number of layers and filters to construct a lightweight UNet. The network segments the low-resolution images and prepares the mask of an organ. Then, we upscale the boundary of the output mask by the Scalable Vector Graphics technique to obtain the final border. This design reduces the number of parameters and the run-time by a factor of two. We segmented several tissues to prove the stability of our method to the type of organ. The experiments proved the feasibility of setting up complex deep neural networks with conventional platforms. [ABSTRACT FROM AUTHOR]

Background: Although the importance of quantitative SPECT has increased tremendously due to newly developed therapeutic radiopharmaceuticals, there are still no accreditation programs to harmonize SPECT imaging. Work is currently underway to develop an accreditation for quantitative 177Lu SPECT/CT. The aim of this study is to verify whether the positioning of the spheres within the phantom has an influence on the recovery and thus needs to be considered in SPECT harmonization. In addition, the effects of these recovery coefficients on a potential partial volume correction as well as absorbed-dose estimates are investigated. Methods: Using a low-dose CT of a SPECT/CT acquisition, a computerized version of the NEMA body phantom was created using a semi-automatic threshold-based method. Based on the mass-density map, the detector orbit, and the sphere centers, realistic SPECT acquisitions of all possible 720 sphere configurations of both the PET and the SPECT versions of the NEMA Body Phantom were generated using Monte Carlo simulations. SPECT reconstructions with different numbers of updates were performed without (CASToR) and with resolution modeling (STIR). Recovery coefficients were calculated for all permutations, reconstruction methods, and phantoms, and their dependence on the sphere positioning was investigated. Finally, the simulation-based findings were validated using SPECT/CT acquisitions of six different sphere configurations. Results: Our analysis shows that sphere positioning has a significant impact on the recovery for both of the reconstruction methods and the phantom type. Although resolution modeling resulted in significantly higher recovery, the relative variation in recovery within the 720 permutations was even larger. When examining the extreme values of the recovery, reconstructions without resolution modeling were influenced primarily by the sphere position, while with resolution modeling the volume of the two adjacent spheres had a larger influence. The SPECT measurements confirmed these observations, and the recovery curves showed good overall agreement with the simulated data. Conclusion: Our study shows that sphere positioning has a significant impact on the recovery obtained in NEMA sphere phantom measurements and should therefore be considered in a future SPECT accreditation. Furthermore, the single-measurement method normally performed for PVC should be reconsidered to account for the position dependency. [ABSTRACT FROM AUTHOR]

Low-Dose computer tomography (LDCT) is an ideal alternative to reduce radiation risk in clinical applications. Although supervised-deep-learning-based reconstruction methods have demonstrated superior performance compared to conventional model-driven reconstruction algorithms, they require collecting massive pairs of low-dose and norm-dose CT images for neural network training, which limits their practical application in LDCT imaging. In this paper, we propose an unsupervised and training data-free learning reconstruction method for LDCT imaging that avoids the requirement for training data. The proposed method is a post-processing technique that aims to enhance the initial low-quality reconstruction results, and it reconstructs the high-quality images by neural work training that minimizes the ℓ1-norm distance between the CT measurements and their corresponding simulated sinogram data, as well as the total variation (TV) value of the reconstructed image. Moreover, the proposed method does not require to set the weights for both the data fidelity term and the plenty term. Experimental results on the AAPM challenge data and LoDoPab-CT data demonstrate that the proposed method is able to effectively suppress the noise and preserve the tiny structures. Also, these results demonstrate the rapid convergence and low computational cost of the proposed method. The source code is available at https://github.com/linfengyu77/IRLDCT. [ABSTRACT FROM AUTHOR]

Photoacoustic imaging (PAI) is an emerging imaging technique that offers real-time, non-invasive, and radiation-free measurements of optical tissue properties. However, image quality degradation due to factors such as non-ideal signal detection hampers its clinical applicability. To address this challenge, this paper proposes an algorithm for super-resolution reconstruction and segmentation based on deep learning. The proposed enhanced deep super-resolution minimalistic network (EDSR-M) not only mitigates the shortcomings of the original algorithm regarding computational complexity and parameter count but also employs residual learning and attention mechanisms to extract image features and enhance image details, thereby achieving high-quality reconstruction of PAI. DeepLabV3+ is used to segment the images before and after reconstruction to verify the network reconstruction performance. The experimental results demonstrate average improvements of 19.76% in peak-signal-to-noise ratio (PSNR) and 4.80% in structural similarity index (SSIM) for the reconstructed images compared to those of their pre-reconstructed counterparts. Additionally, mean accuracy, mean intersection and union ratio (IoU), and mean boundary F1 score (BFScore) for segmentation showed enhancements of 8.27%, 6.20%, and 6.28%, respectively. The proposed algorithm enhances the effect and texture features of PAI and makes the overall structure of the image restoration more complete. [ABSTRACT FROM AUTHOR]

Significance: Photoacoustic computed tomography (PACT) is a promising noninvasive imaging technique for both life science and clinical implementations. To achieve fast imaging speed, modern PACT systems have equipped arrays that have hundreds to thousands of ultrasound transducer (UST) elements, and the element number continues to increase. However, large number of UST elements with parallel data acquisition could generate a massive data size, making it very challenging to realize fast image reconstruction. Although several research groups have developed GPU-accelerated method for PACT, there lacks an explicit and feasible step-by-step description of GPU-based algorithms for various hardware platforms. Aim: In this study, we propose a comprehensive framework for developing GPU-accelerated PACT image reconstruction (GPU-accelerated photoacoustic computed tomography), to help the research community to grasp this advanced image reconstruction method. Approach: We leverage widely accessible open-source parallel computing tools, including Python multiprocessing-based parallelism, Taichi Lang for Python, CUDA, and possible other backends. We demonstrate that our framework promotes significant performance of PACT reconstruction, enabling faster analysis and real-time applications. Besides, we also described how to realize parallel computing on various hardware configurations, including multicore CPU, single GPU, and multiple GPUs platform. Results: Notably, our framework can achieve an effective rate of ~871 times when reconstructing extremely large-scale three-dimensional PACT images on a dual-GPU platform compared to a 24-core workstation CPU. In this paper, we share example codes via GitHub. Conclusions: Our approach allows for easy adoption and adaptation by the research community, fostering implementations of PACT for both life science and medicine. [ABSTRACT FROM AUTHOR]

In recent years, many methods for image super‐resolution (SR) have relied on pairs of low‐resolution (LR) and high‐resolution (HR) images for training, where the degradation process is predefined by bicubic downsampling. While such approaches perform well in standard benchmark tests, they often fail to accurately replicate the complexity of real‐world image degradation. To address this challenge, researchers have proposed the use of unpaired image training to implicitly model the degradation process. However, there is a significant domain gap between the real‐world LR and the synthetic LR images from HR, which severely degrades the SR performance. A novel unsupervised image‐blind super‐resolution method that exploits degradation feature‐based learning for real‐image super‐resolution reconstruction (RDFL) is proposed. Their approach learns the degradation process from HR to LR using a generative adversarial network (GAN) and constrains the data distribution of the synthetic LR with real degraded images. The authors then encode the degraded features into a Transformer‐based SR network for image super‐resolution reconstruction through degradation representation learning. Extensive experiments on both synthetic and real datasets demonstrate the effectiveness and superiority of the RDFL method, which achieves visually pleasing reconstruction results. [ABSTRACT FROM AUTHOR]

Quasi-continuous-phase metasurfaces overcome the side effects imposed by high-order diffraction on imaging and can impart optical parameters such as amplitude, phase, polarization, and frequency to incident light at sub-wavelength scales with high efficiency. Structured-light three-dimensional (3D) imaging is a hot topic in the field of 3D imaging because of its advantages of low computation cost, high imaging accuracy, fast imaging speed, and cost-effectiveness. Structured-light 3D imaging requires uniform diffractive optical elements (DOEs), which could be realized by quasi-continuous-phase metasurfaces. In this paper, we design a quasi-continuous-phase metasurface beam splitter through a vector iterative Fourier transform algorithm and utilize this device to realize structured-light 3D imaging of a target object with subsequent target reconstruction. A structured-light 3D imaging system is then experimentally implemented by combining the fabricated quasi-continuous-phase metasurface illuminated by the vertical-cavity surface-emitting laser and a binocular recognition system, which eventually provides a new technological path for the 3D imaging field. [ABSTRACT FROM AUTHOR]

In poor lighting and rainy and foggy bad weather environments, road traffic signs are blurred and have low recognition, etc. A super-resolution reconstruction algorithm for complex lighting and bad weather traffic sign images was proposed. First, a novel attention residual module was designed to incorporate an aggregated feature attention mechanism on the jump connection side of the base residual module so that the deep network can obtain richer detail information; second, a cross-layer jump connection feature fusion mechanism was adopted to enhance the flow of information across layers as well as to prevent the problem of gradient disappearance of the deep network to enhance the reconstruction of the edge detail information; and lastly, a positive-inverse dual-channel sub-pixel convolutional up-sampling method was designed to reconstruct super-resolution images to obtain better pixel and spatial information expression. The evaluation model was trained on the Chinese traffic sign dataset in a natural scene, and when the scaling factor is 4, the average values of PSNR and SSIM are improved by 0.031 when compared with the latest release of the deep learning-based superresolution reconstruction algorithm for single-frame images, MICU (Multi-level Information Compensation and U-net), the average values of PSNR and SSIM are improved by 0.031 dB and 0.083, and the actual test average reaches 20.946 dB and 0.656. The experimental results show that the reconstructed image quality of this paper's algorithm is better than the mainstream algorithms of comparison in terms of objective indexes and subjective feelings. The super-resolution reconstructed image has a higher peak signal-to-noise ratio and perceptual similarity. It can provide certain technical support for the research of safe driving assistive devices in natural scenes under multi-temporal varying illumination conditions and bad weather. [ABSTRACT FROM AUTHOR]

Purpose: To quantify operator dose in interventional CT fluoroscopy (CTF) procedures and compare to catheterization laboratory and interventional radiology procedures. Methods: Fifteen studies quantifying operator scatter dose for c‐arm fluoroscopy and CTF were reviewed from a literature survey. The dose provided in each study was normalized to skin entrance air kerma and air kerma rate including backscatter. The average air kerma rate and exposure duration were calculated and compared between c‐arm fluoroscopy and CTF. To enable operator scatter dose calculations in CTF procedures, a model was created which scaled the dose from a reference condition. Results: Our literature survey indicated that for CTF relative to c‐arm fluoroscopy: (1) the mean air kerma rate is 3.5 times higher, while (2) the mean exposure duration is 31.6 times lower. On average, a physician would have to perform 9.1 times (i.e., 31.6/3.5) more CTF procedures relative to c‐arm fluoroscopy procedure to receive equal operator scatter doses. Our own experimental measurements provided scaling factors allowing physician scatter dose to be obtained at any rotation time, tube current, beam collimation, or beam energy. Conclusion: Our results shed light onto a currently mis‐understood issue in radiology. Namely, that physicians receive more dose from CT relative to c‐arm fluoroscopy motivating them to retreat from the room during interventional exposures. Our literature review and experimental measurements indicate the opposite it true; interventional CT produces on average nine times less physician dose relative to c‐arm procedures. Our method allows for prospective/retrospective determination of an individual's dose. [ABSTRACT FROM AUTHOR]

The hyperspectral image (HSI) distinguishes itself in material identification through its exceptional spectral resolution. However, its spatial resolution is constrained by hardware limitations, prompting the evolution of HSI super-resolution (SR) techniques. Single HSI SR endeavors to reconstruct high-spatial-resolution HSI from low-spatial-resolution inputs, and recent progress in deep learning-based algorithms has significantly advanced the quality of reconstructed images. However, convolutional methods struggle to extract comprehensive spatial and spectral features. Transformer-based models have yet to harness long-range dependencies across both dimensions fully, thus inadequately integrating spatial and spectral data. To solve the above problem, in this paper, we propose a new HSI SR method, SSAformer, which merges the strengths of CNNs and Transformers. It introduces specially designed attention mechanisms for HSI, including spatial and spectral attention modules, and overcomes the previous challenges in extracting and amalgamating spatial and spectral information. Evaluations on benchmark datasets show that SSAformer surpasses contemporary methods in enhancing spatial details and preserving spectral accuracy, underscoring its potential to expand HSI's utility in various domains, such as environmental monitoring and remote sensing. [ABSTRACT FROM AUTHOR]

Standard beams are mainly used for the calibration of strain sensors using their load reconstruction models. However, as an ill-posed inverse problem, the solution to these models often fails to converge, especially when dealing with dynamic loads of different frequencies. To overcome this problem, a piecewise Tikhonov regularization method (PTR) is proposed to reconstruct dynamic loads. The transfer function matrix is built both using the denoised excitations and the corresponding responses. After singular value decomposition (SVD), the singular values are divided into submatrices of different sizes by utilizing a piecewise function. The regularization parameters are solved by optimizing the piecewise submatrices. The experimental result shows that the MREs of the PTR method are 6.20% at 70 Hz and 5.86% at 80 Hz. The traditional Tikhonov regularization method based on GCV exhibits MREs of 28.44% and 29.61% at frequencies of 70 Hz and 80 Hz, respectively, whereas the L-curve-based approach demonstrates MREs of 29.98% and 18.42% at the same frequencies. Furthermore, the PREs of the PTR method are 3.54% at 70 Hz and 3.73% at 80 Hz. The traditional Tikhonov regularization method based on GCV exhibits PREs of 27.01% and 26.88% at frequencies of 70 Hz and 80 Hz, respectively, whereas the L-curve-based approach demonstrates PREs of 29.50% and 15.56% at the same frequencies. All in all, the method proposed in this paper can be extensively applied to load reconstruction across different frequencies. [ABSTRACT FROM AUTHOR]

This paper proposes a super‐resolution reconstruction model, SRPGANto improve the visual quality of images based on generative adversarial networks (GANs) by improving the network structures of the generator and the discriminator. In the generator, a dual branch residual block is designed instead of the residual block, including a branch with an attention mechanism and a branch without an attention mechanism, to extract more differentiated features. Normalization methods are explored to avoid unstable training and bath normalization artifacts and use a half instance normalization layer that is more suitable for underlying visual problems compared with traditional batch normalization. In the discriminator, PatchGAN is applied instead of typical GAN to improve the generation of local texture by discriminating each patch rather than the global image. The experimental results on the public datasets demonstrate that the proposed SRPGAN can achieve excellent quantitative evaluation while improving the visual quality of reconstructed images. [ABSTRACT FROM AUTHOR]

For the significant distortion problem caused by the special projection method of equi-rectangular projection (ERP) images, this paper proposes an omnidirectional image super-resolution algorithm model based on position information transformation, taking SwinIR as the base. By introducing a space position transformation module that supports deformable convolution, the image preprocessing process is optimized to reduce the distortion effects in the polar regions of the ERP image. Meanwhile, by introducing deformable convolution in the deep feature extraction process, the model's adaptability to local deformations of images is enhanced. Experimental results on publicly available datasets have shown that our method outperforms SwinIR, with an average improvement of over 0.2 dB in WS-PSNR and over 0.030 in WS-SSIM for ×4 pixel upscaling. [ABSTRACT FROM AUTHOR]

To precisely estimate blur kernels (BKs) and obtain high-quality latent images (LIs), we present an efficient blind image deblurring (BID) algorithm via a gradient prior and an enhanced sparse prior. The proposed algorithm decomposes BID into two phases: multiscale blur estimation (MSBE) and image deconvolution (ID). The former focuses on precise BKs, whereas the latter focuses on high-quality LIs. In the MSBE phase, significant image gradients and an enhanced sparse prior are used to estimate BKs. To this end, we build an MSBE model using a hyper-Laplacian gradient (HLG) prior and sparsity-inducing l2,1-regularization. l2,1-regularization imposes enhanced sparsity on the BKs to be estimated, and the HLG prior characterizes the true distribution of image gradients. We use a method derived from half-quadratic splitting (HQS) to handle the built MSBE model by decoupling it into simpler subproblems. Subsequently, these subproblems are solved by efficient and concise methods. In the MSBE phase, we also adopt a multiscale process to avoid local optima of the estimated BKs and adaptively update a penalty parameter to accelerate the iteration. In the ID phase, SotA restoration algorithms are employed by the proposed BID algorithm to obtain competitive final LIs. The experiments are conducted on several baseline datasets to evaluate our BID algorithm. Under the evaluation criteria of success-rate (SR), SSDE, PSNR, SSIM, vision effect, and running time (RT), our study shows apparent advantages compared with some SotA BID algorithms. These advantages include but are not limited to reducing the RT; obtaining higher PSNRs, SSIMs, and SRs; decreasing SSDEs; and obtaining high-quality LIs without artifacts. [ABSTRACT FROM AUTHOR]

Image reconstruction in multispectral computed tomography (MSCT) requires solving a challenging nonlinear inverse problem, commonly tackled via iterative optimization algorithms. Existing methods necessitate computing the derivative of the forward map and potentially its regularized inverse. In this work, we present a simple yet highly effective algorithm for MSCT image reconstruction, utilizing iterative update mechanisms that leverage the full forward model in the forward step and a derivative-free adjoint problem. Our approach demonstrates both fast convergence and superior performance compared to existing algorithms, making it an interesting candidate for future work. We also discuss further generalizations of our method and its combination with additional regularization and other data discrepancy terms. [ABSTRACT FROM AUTHOR]

The reconstruction of three-dimensional scenes from a single image represents a significant challenge in computer vision, particularly in the context of cultural heritage digitisation, where datasets may be limited or of poor quality. This paper addresses this challenge by conducting a study of the latest and most advanced algorithms for single-image 3D reconstruction, with a focus on applications in cultural heritage conservation. Exploiting different single-image datasets, the research evaluates the strengths and limitations of various artificial intelligence-based algorithms, in particular Neural Radiance Fields (NeRF), in reconstructing detailed 3D models from limited visual data. The study includes experiments on scenarios such as inaccessible or non-existent heritage sites, where traditional photogrammetric methods fail. The results demonstrate the effectiveness of NeRF-based approaches in producing accurate, high-resolution reconstructions suitable for visualisation and metric analysis. The results contribute to advancing the understanding of NeRF-based approaches in handling single-image inputs and offer insights for real-world applications such as object location and immersive content generation. [ABSTRACT FROM AUTHOR]

Large-area image acquisition techniques are essential in underwater investigations: high-resolution 3D image-based reconstructions have improved coral reef monitoring by enabling novel seascape ecological analysis. Artificial intelligence (AI) offers methods for significantly accelerating image data interpretation, such as automatically recognizing, enumerating, and measuring organisms. However, the rapid proliferation of these technological achievements has led to a relative lack of standardization of methods. Remarkably, there are notable differences in procedures for generating human and AI annotations, and there is also a scarcity of publicly available datasets and shared machine-learning models. The lack of standard procedures makes it challenging to compare and reproduce scientific findings. One way to overcome this problem is to make the most used platforms by coral reef scientists interoperable so that the analyses can all be exported into a common format. This paper introduces functionality to promote interoperability between three popular open-source software tools dedicated to the digital study of coral reefs: TagLab, CoralNet, and Viscore. As users of each platform may have different analysis pipelines, we discuss several workflows for managing and processing point and area annotations, improving collaboration among these tools. Our work sets the foundation for a more seamless ecosystem that maintains the established investigation procedures of various laboratories but allows for easier result sharing. [ABSTRACT FROM AUTHOR]

The single-image super-resolution task benefits has a wide range of application scenarios, so has long been a hotspot in the field of computer vision. However, designing a continuous-scale super-resolution algorithm with excellent performance is still a difficult problem to solve. In order to solve this problem, we propose a continuous-scale SR algorithm based on a Transformer, which is called residual dense Swin Transformer (RDST). Firstly, we design a residual dense Transformer block (RDTB) to enhance the information flow before and after the network and extract local fusion features. Then, we use multilevel feature fusion to obtain richer feature information. Finally, we use the upsampling module based on the local implicit image function (LIIF) to obtain continuous-scale super-resolution results. We test RDST on multiple benchmarks. The experimental results show that RDST achieves SOTA performance in the fixed scale of super-resolution tasks in the distribution, and significantly improves (0.1∼0.6 dB) the arbitrary scale of super-resolution tasks out of distribution. Sufficient experiments show that our RDST can use fewer parameters, and its performance is better than the SOTA SR method. [ABSTRACT FROM AUTHOR]

This article proposes a target image processing method based on super-resolution reconstruction and machine learning algorithms, which solves the low-resolution problem in medical images during imaging. This method uses nonlocal autoregressive learning based on a medical image super-resolution reconstruction method. The autoregressive model is introduced into the sparse representation-based medical image super-resolution reconstruction model by utilizing medical image data inherent nonlocal similarity characteristics. At the same time, a clustering algorithm is used to obtain a classification dictionary, improving experimental efficiency. The experimental results show that ten randomly selected CT/MR images are used as test images, and each image's peak signal-to-noise ratio and structural similarity values are calculated separately. Compared with other methods, the method proposed in this paper is significantly better and can achieve ideal results, with the highest value being 31.49. This method demonstrates the feasibility of using super-resolution reconstruction and machine learning algorithms in medical image resolution. [ABSTRACT FROM AUTHOR]

A new generation cone‐beam computed tomography (CBCT) system with new hardware design and advanced image reconstruction algorithms is available for radiation treatment simulation or adaptive radiotherapy (HyperSight CBCT imaging solution, Varian Medical Systems‐a Siemens Healthineers company). This study assesses the CBCT image quality metrics using the criteria routinely used for diagnostic CT scanner accreditation as a first step towards the future use of HyperSight CBCT images for treatment planning and target/organ delineations. Image performance was evaluated using American College of Radiology (ACR) Program accreditation phantom tests for diagnostic computed tomography systems (CTs) and compared HyperSight images with a standard treatment planning diagnostic CT scanner (Siemens SOMATOM Edge) and with existing CBCT systems (Varian TrueBeam version 2.7 and Varian Halcyon version 2.0). Image quality performance for all Varian HyperSight CBCT vendor‐provided imaging protocols were assessed using ACR head and body ring CT phantoms, then compared to existing imaging modalities. Image quality analysis metrics included contrast‐to‐noise (CNR), spatial resolution, Hounsfield number (HU) accuracy, image scaling, and uniformity. All image quality assessments were made following the recommendations and passing criteria provided by the ACR. The Varian HyperSight CBCT imaging system demonstrated excellent image quality, with the majority of vendor‐provided imaging protocols capable of passing all ACR CT accreditation standards. Nearly all (8/11) vendor‐provided protocols passed ACR criteria using the ACR head phantom, with the Abdomen Large, Pelvis Large, and H&N vendor‐provided protocols produced HU uniformity values slightly exceeding passing criteria but remained within the allowable minor deviation levels (5–7 HU maximum differences). Compared to other existing CT and CBCT imaging modalities, both HyperSight Head and Pelvis imaging protocols matched the performance of the SOMATOM CT scanner, and both the HyperSight and SOMATOM CT substantially surpassed the performance of the Halcyon 2.0 and TrueBeam version 2.7 systems. Varian HyperSight CBCT imaging system could pass almost all tests for all vendor‐provided protocols using ACR accreditation criteria, with image quality similar to those produced by diagnostic CT scanners and significantly better than existing linac‐based CBCT imaging systems. [ABSTRACT FROM AUTHOR]

Vector Quantization (VQ) is a technique with a wide range of applications. For example, it can be used for image compression. The codebook design for VQ has great significance in the quality of the quantized signals and can benefit from the use of swarm intelligence. Initialization of the Linde–Buzo–Gray (LBG) algorithm, which is the most popular VQ codebook design algorithm, is a step that directly influences VQ performance, as the convergence speed and codebook quality depend on the initial codebook. A widely used initialization alternative is random initialization, in which the initial set of codevectors is drawn randomly from the training set. Other initialization methods can lead to a better quality of the designed codebooks. The present work evaluates the impacts of initialization strategies on swarm intelligence algorithms for codebook design in terms of the quality of the designed codebooks, assessed by the quality of the reconstructed images, and in terms of the convergence speed, evaluated by the number of iterations. Initialization strategies consist of a combination of codebooks obtained by initialization algorithms from the literature with codebooks composed of vectors randomly selected from the training set. The possibility of combining different initialization techniques provides new perspectives in the search for the quality of the VQ codebooks. Nine initialization strategies are presented, which are compared with random initialization. Initialization strategies are evaluated on the following algorithms for codebook design based on swarm clustering: modified firefly algorithm—Linde–Buzo–Gray (M-FA-LBG), modified particle swarm optimization—Linde–Buzo–Gray (M-PSO-LBG), modified fish school search—Linde–Buzo–Gray (M-FSS-LBG) and their accelerated versions (M-FA-LBGa, M-PSO-LBGa and M-FSS-LBGa) which are obtained by replacing the LBG with the accelerated LBG algorithm. The simulation results point out to the benefits of the proposed initialization strategies. The results show gains up to 4.43 dB in terms of PSNR for image Clock with M-PSO-LBG codebooks of size 512 and codebook design time savings up to 67.05% for image Clock, with M-FF-LBGa codebooks with size N = 512 , by using initialization strategies in substitution to Random initialization. [ABSTRACT FROM AUTHOR]

Due to the excellent characteristics of Synthetic Aperture Radar (SAR), it has become an important technical means for land and sea target detection applications worldwide. However, the SAR image imaging process is often accompanied by speckle noise, which affects the accuracy of target detection. For this problem, this paper proposes an SAR image reconstruction model based on image denoising and feature extraction. Given a noisy SAR image, at each pixel of the image, the value of the singularity index of a metric defined by the signal gradient norm density is calculated. Then through a hierarchical decomposition of these singular indices, a set of pixels containing the most informative feature set of the image is extracted, and a denoised image is reconstructed using the extracted features. In comparison to traditional filtering and denoising techniques such as non-local filtering, this algorithm achieves remarkable local filtering and denoising effects while preserving the image's texture information. The denoising performance of the proposed method is evaluated through a series of quantitative indicators and visual comparisons. The results demonstrate that this method effectively reduces background clutter, resulting in a higher contrast between the target and the background. [ABSTRACT FROM AUTHOR]

Aim: High-sensitivity hybrid positron emission tomography (PET) imaging using advanced whole-body (WB) or total-body PET/computed tomography (CT) systems permits reducing injected tracer activity while preserving diagnostic quality. Such approaches are promising for healthy control studies or exploring inter-organ communication in systemic diseases. This study assessed test/retest variations in the fluoro-2-deoxy-D-glucose (FDG) uptake in key organs from low-dose (LD) and standard-dose (STD) [18F]FDG-PET/CT imaging protocols in healthy controls and lung cancer patients. Methods: A total of 19 healthy controls (19-62 years, 46-104 kg, 10 M/9 F) and 7 lung cancer patients (47-77 years, 50-88 kg, 4 M/3 F) underwent [18F]FDGPET/ CT imaging. All subjects were first injected ("test," LD) with 28 ± 2 MBq FDG and underwent a dynamic (0-67 min post-injection) WB imaging protocol with LD-CT. Then, 90 min post-LD injection, the subjects were repositioned and injected with 275 ± 16 MBq FDG ("retest," STD). Second LD-CT and STD-CT scans were acquired for healthy controls and patients, respectively. Static images (55-67 min post-injection) were considered for subsequent analysis. The CT images were used to automatically segment the target volumes of interest. Standardized uptake values normalized to the body weight (SUV[sub BW]) were extracted for each volume of interest. The mean SUV[sub BW] were compared for both LD/STD conditions with paired t-tests. In patients, FDG-avid lesions were manually delineated on LD and STD static images. Effective dose levels were estimated from both the CT and PET acquisitions. Results: Organ-based mean SUV[sub BW] were similar between the LD and STD (mean %difference =5%) in both healthy controls and cancer patients, except in the heart. Intra-control test/retest variability was significant in the brain, heart, and skeletal muscle (p < 0.05). While 17 lesions were delineated on the STD images of the patients, only 10/17 lesions were identified on the LD images due to increased image noise. Lesion-based mean SUV[sub BW] were similar between LD and STD acquisitions (p = 0.49, %difference = 10%). In patients, the effective doses were (1.9 ± 0.2) mSv (LD-CT), (16.6 ± 5.4) mSv (STD-CT), (0.5 ± 0.1) mSv (LD-PET), and (4.6 ± 0.3) mSv (STD-PET). Conclusion: LD and STD [18F]FDG injections in healthy controls and lung cancer patients yielded comparable mean SUV[sub BW], except in the heart. Dose levels may be reduced for [18F]FDG-PET imaging without a loss in mean SUV[sub BW] accuracy, promoting LD-PET/CT protocols for studying multi-organ metabolic patterns. In oncology patients, this approach may be hindered by a lower diagnostic quality in the presence of significant noise. [ABSTRACT FROM AUTHOR]

The three-dimensional (3D) reconstruction of Electromagnetic Tomography (EMT) is an important task for many applications, such as the non-destructive testing of inner defects in rail systems. Additionally, image reconstruction algorithms utilizing deep learning methods have been verified to be useful in recent years. Therefore, the interpretability of deep learning is a question that is relevant to its application in other areas. This paper proposes an innovative rotational convolution pattern, Conv-P, for convolutional neural network (CNN) image reconstruction in a 3D EMT system. This pattern is based on the projection relationships inherent in tomographic imaging, where each convolution is performed on adjacent projections along the excitation rotation direction. The advantage of this pattern is that it can generate the convolution process by utilizing the 3D structural information from real sensors. To verify the effectiveness of this convolution pattern, we constructed a 3D dual-layer 16-coil EMT model and tested its image reconstruction performance. The results demonstrate that, compared with two common convolution patterns, Conv-P achieves a 4.7% and 4.1% increase in the Image Correlation Coefficient (CC), a 19.8% and 13.1% reduction in the Relative Image Error (IE), a 0.67% and 1.59% increase in the Peak Signal-to-Noise Ratio (PSNR), and a 3.24% and 0.74% increase in the Structural Similarity Index Measure (SSIM). [ABSTRACT FROM AUTHOR]

(k , n) threshold progressive secret image sharing (PSIS) has become an important issue in recent years. In (k , n) PSIS, a secret image is encrypted into n shadows such that k to n shadows can gradually reconstruct the secret image. Since an image can usually be divided into different regions in such a way that each region includes information with different importance levels, region-based PSIS has also been proposed where the reconstruction of different regions requires different thresholds on the shadow numbers. In this work, we propose new region-based (k , n) PSIS that achieves a novel reconstruction model, where all regions possess the property of (k , n) threshold progressive reconstruction, but the same number of shadows recovers a lower proportion of information in regions with a higher importance level. This new reconstruction model can further complete the application of region-based PSIS, where each region has an equal minimum threshold for reconstruction, and the difference in importance levels between regions can be reflected in the proportion of the recovered image using the same number of shadows. A theoretical analysis proves the correctness of the proposed scheme, and the experimental results from four secret images also show the practicality and effectiveness of the proposed scheme. [ABSTRACT FROM AUTHOR]

As a distributed learning paradigm, federated learning is supposed to protect data privacy without exchanging users' local data. Even so, the gradient inversion attack, in which the adversary can reconstruct the original data from shared training gradients, has been widely deemed as a severe threat. Nevertheless, most existing researches are confined to impractical assumptions and narrow range of applications. To mitigate these shortcomings, we propose a comprehensive framework for gradient inversion attack, with well-designed algorithms for image and label reconstruction. For image reconstruction, we fully utilize the generative image prior, which derives from wide-used generative models, to improve the reconstructed results, by additional means of iterative optimization on mixed spaces and gradient-free optimizer. For label reconstruction, we design an adaptive recovery algorithm regarding real data distribution, which can adjust previous attacks to more complex scenarios. Moreover, we incorporate a gradient approximation method to efficiently fit our attack for FedAvg scenario. We empirically verify our attack framework using benchmark datasets and ablation studies, considering loose assumptions and complicated circumstances. We hope this work can greatly reveal the necessity of privacy protection in federated learning, while urge more effective and robust defense mechanisms. [ABSTRACT FROM AUTHOR]

Single image super‐resolution (SISR) has gained significant attention in image processing and computer vision, driven by deep learning‐based models like convolutional neural networks (CNN). Yet, the resource‐intensive nature of these models poses challenges when deploying them on edge devices. To address this issue, resource‐constrained models need to be developed. While recent models like the information distillation network (IDN), the information multi‐distillation network (IMDN), the residual feature distillation network (RFDN), and so on, have attempted to reduce parameters and computational complexity, further optimization remains vital. Therefore, this paper presents an approach to enhancing the efficiency and lightweight nature of the SISR. We introduce a novel lightweight SR model by building upon the RFDN architecture, the winner of the AIM2020 and NTIRE2022 SR challenges. The proposed depthwise channel attention network (DWCAN) model makes some key changes to RFDN. First, it replaces the main residual feature distillation block (RFDB) with a depthwise channel attention block (DWCAB). Additionally, DWCAN includes a shallow residual block (SRB) with depthwise separable convolution (DW) and a channel attention (CA) block. The primary goal of our work is to significantly reduce model parameters, computational operations, inference time, and memory size while maintaining or improving a peak signal‐to‐noise ratio (PSNR) of 29 dB. The experimental results demonstrate the effectiveness of the proposed model. By applying our modifications, we achieve a notable reduction in model complexity, leading to an improved PSNR of 29.07 dB, up from RFDN's 29.04 dB on a diverse 2 K resolution (DIV2K) dataset. This underscores the potential of our lightweight model to balance computational efficiency and SR quality. Additionally, the proposed work is essential for the metaverse for two key reasons: (1) Enhancing visual quality by adding complex details to textures and objects, making the digital world feel more like reality. (2) Ensuring device compatibility across a range of gadgets, from smartphones to VR headsets, optimizing the metaverse experience for all users. In short, a lightweight single image super‐resolution model for image reconstruction is proposed in this paper. [ABSTRACT FROM AUTHOR]

Thermal infrared remotely sensed data, by capturing the thermal radiation characteristics emitted by the Earth's surface, plays a pivotal role in various domains, such as environmental monitoring, resource exploration, agricultural assessment, and disaster early warning. However, the acquisition of thermal infrared hyperspectral remotely sensed imagery necessitates more complex and higher-precision sensors, which in turn leads to higher research and operational costs. In this study, a novel Convolutional Neural Network (CNN)–Transformer combined block, termed CTBNet, is proposed to address the challenge of thermal infrared multispectral image spectral reconstruction. Specifically, the CTBNet comprises blocks that integrate CNN and Transformer technologies (CTB). Within these CTBs, an improved self-attention mechanism is introduced, which not only considers features across spatial and spectral dimensions concurrently, but also explicitly extracts incremental features from each channel. Compared to other algorithms, the proposed method more closely aligns with the true spectral curves in the reconstruction of hyperspectral images across the spectral dimension. Through a series of experiments, this approach has been proven to ensure robustness and generalizability, outperforming some state-of-the-art algorithms across various metrics. [ABSTRACT FROM AUTHOR]