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Background: Blurry images in teledermatology and consultation increased the diagnostic difficulty for both deep learning models and physicians. We aim to determine the extent of restoration in diagnostic accuracy after blurry images are deblurred by deep learning models. Methods: We used 19,191 skin images from a public skin image dataset that includes 23 skin disease categories, 54 skin images from a public dataset of blurry skin images, and 53 blurry dermatology consultation photos in a medical center to compare the diagnosis accuracy of trained diagnostic deep learning models and subjective sharpness between blurry and deblurred images. We evaluated five different deblurring models, including models for motion blur, Gaussian blur, Bokeh blur, mixed slight blur, and mixed strong blur. Main Outcomes and Measures: Diagnostic accuracy was measured as sensitivity and precision of correct model prediction of the skin disease category. Sharpness rating was performed by board-certified dermatologists on a 4-point scale, with 4 being the highest image clarity. Results: The sensitivity of diagnostic models dropped 0.15 and 0.22 on slightly and strongly blurred images, respectively, and deblurring models restored 0.14 and 0.17 for each group. The sharpness ratings perceived by dermatologists improved from 1.87 to 2.51 after deblurring. Activation maps showed the focus of diagnostic models was compromised by the blurriness but was restored after deblurring. Conclusions: Deep learning models can restore the diagnostic accuracy of diagnostic models for blurry images and increase image sharpness perceived by dermatologists. The model can be incorporated into teledermatology to help the diagnosis of blurry images. [ABSTRACT FROM AUTHOR]

Low-light image enhancement algorithms have been widely developed. Nevertheless, using long exposure under low-light conditions will lead to motion blurs of the captured images, which presents a challenge to address low-light enhancement and deblurring jointly. A recent effort called LEDNet addresses these issues by designing a encoder-decoder pipeline. However, LEDNet relies on paired data during training, but capturing low-blur and normal-sharp images of the same visual scene simultaneously is challenging. To overcome these challenges, we propose a self-supervised normalizing flow called SSFlow for jointing low-light enhancement and deblurring. SSFlow consists of two modules: an orthogonal channel attention U-Net (OAtt-UNet) module for extracting features, and a normalizing flow for correcting color and denoising (CCD flow). During the training of the SSFlow, the two modules are connected to each other by a color map. Concretely, OAtt-UNet module is a variant of U-Net consisting of an encoder and a decoder. OAtt-UNet module takes a low-light blurry image as input, and incorporates an orthogonal channel attention block into the encoder to improve the representation ability of the overall network. The filter adaptive convolutional layer is integrated into the decoder, applying a dynamic convolution filter to each element of the feature for effective deblurring. To extract color information and denoise, the CCD flow makes full use of the powerful learning ability of the normalizing flow. We construct an unsupervised loss function, continuously optimizing the network by using the consistent color map between the two modules in the color space. The effectiveness of our proposed network is demonstrated through both qualitative and quantitative experiments. Code is available at https://github.com/shibaoshun/SSFlow. [ABSTRACT FROM AUTHOR]

Poisson image deblurring, which aims to restore the latent image from its blurred and noisy observation, has drawn significant attention in image processing. Due to its ill-posed nature, enhancing image quality often involves incorporating a well-defined prior to effectively regularize the ill-posed inverse problem. Building upon the framelet system, we propose a frame-based nonconvex regularization method for Poisson image deblurring. The method is formulated by combining a data-fitting term with the difference of two norms, namely ℓ 1 and ℓ 2 , on the latent image. We solve the optimization problem by combining the difference of convex functions algorithm (DCA) with the alternating direction method of multipliers (ADMM), establishing its convergence. We further employ a simulated annealing procedure and show that proposed algorithm almost certainly converges to a global minimum. Two different approaches are employed to handle the frame-based ℓ 1 norm within the ADMM framework. In particular, the frame-based nonconvex regularization method is also considered for the blind Poisson problem. An effective recovery model and its algorithm are presented. Experimental results demonstrate the effectiveness of our proposed models compared with other models in terms of quantitative metrics and visual quality. • Frame-based nonconvex regularization model is proposed to eliminate Poisson noise and blur from degraded image. • An efficient algorithm with convergence guarantees is proposed. • We study an accelerated algorithm and explore the blind Poisson problem. • Experimental results demonstrate the effectiveness of our proposed models compared with other models. [ABSTRACT FROM AUTHOR]

This article proposes a novel "Deep Salient Image Deblurring (DSID) Framework" for kernel-free image deblurring that combines saliency detection and variational autoencoders and Wasserstein generative adversarial network (VAEWGAN). The first phase is saliency-guided extraction, which is a pre-processing technique. The next phase is classification, which is done by VAEWGAN. It maps the blurred image into deblurred image. The performance metrics, like PSNR, SSIM and NC, image enhancement factor (IEF), and root mean square error (RMSE), are analysed. The proposed method attains of 35.66%, 41.22%, and 27.66% and higher normalized cross-correlation of 22.15%, 18.97% and 14.29% compared with the existing systems, like tuning-free plug-and-play hyper-spectral image deconvolution with deep priors (B3DNN-ADMM), depth estimation along image restoration utilizing deep learning from defocused images (DFD-2HDED.NE), and INFWIDE: image with feature space Wiener deconvolution network for non-blind image deblurring (INFWIDE-ID). [ABSTRACT FROM AUTHOR]

Blurring is a significant concern in electro-optical satellite imagery due to its negative effect on image quality, which is caused by an undesirable loss of bandwidth.Blurred images and compromised image quality may result from atmospheric distortions, camera aberrations, and relative motion during the imaging process. Deblurring is thus a process used to restore deteriorated images, reduce blur, and recover the original image. The major type of blur explored in this study is Gaussian blur, and its effects on the image are investigated by applying the blur in equal proportions. Furthermore, influence of blur on the Modulation Transfer Function (MTF) of an electro-optic satellite image as well as the impacts of deblurring techniques, namely the Richardson-Lucy Deconvolution Algorithm, the Regularization Filter, and the Blind Deconvolution Algorithm on MTF values, wereexplored.

Building occupancy information is significant for a variety of reasons, from allocation of resources in smart buildings to responding during emergency situations. As most people spend more than 90% of their time indoors, a comfortable indoor environment is crucial. To ensure comfort, traditional HVAC systems condition rooms assuming maximum occupancy, accounting for more than 50% of buildings' energy budgets in the US. Occupancy level is a key factor in ensuring energy efficiency, as occupancy-controlled HVAC systems can reduce energy waste by conditioning rooms based on actual usage. Numerous studies have focused on developing occupancy estimation models leveraging existing sensors, with camera-based methods gaining popularity due to their high precision and widespread availability. However, the main concern with using cameras for occupancy estimation is the potential violation of occupants' privacy. Unlike previous video-/image-based occupancy estimation methods, we addressed the issue of occupants' privacy in this work by proposing and investigating both motion-based and motion-independent occupancy counting methods on intentionally blurred video frames. Our proposed approach included the development of a motion-based technique that inherently preserves privacy, as well as motion-independent techniques such as detection-based and density-estimation-based methods. To improve the accuracy of the motion-independent approaches, we utilized deblurring methods: an iterative statistical technique and a deep-learning-based method. Furthermore, we conducted an analysis of the privacy implications of our motion-independent occupancy counting system by comparing the original, blurred, and deblurred frames using different image quality assessment metrics. This analysis provided insights into the trade-off between occupancy estimation accuracy and the preservation of occupants' visual privacy. The combination of iterative statistical deblurring and density estimation achieved a 16.29% counting error, outperforming our other proposed approaches while preserving occupants' visual privacy to a certain extent. Our multifaceted approach aims to contribute to the field of occupancy estimation by proposing a solution that seeks to balance the trade-off between accuracy and privacy. While further research is needed to fully address this complex issue, our work provides insights and a step towards a more privacy-aware occupancy estimation system. [ABSTRACT FROM AUTHOR]

Automatic imaging monitoring technology has been developed as a mature solution to address oil spill accidents. However, when airborne equipment is used to monitor the area of leakage, there is a chance of relative motion between the equipment and the target, leading to blurred images. This can hinder subsequent monitoring tasks, such as the identification and segmentation of hazardous chemical targets. Therefore, it is crucial to deblur these images, as sharper images can substantially improve the precision and accuracy of these tasks. In light of this, this paper presents a novel multi-scale recurrent deblurring network, leveraging the Convolutional Block Attention Module (CBAM) attention mechanism, to restore clear images in an end-to-end manner. The CBAM attention mechanism has been incorporated, which learns features in both the spatial and channel domains, thereby enhancing the deblurring ability of the network by combining the attention mechanisms of multiple domains. To evaluate the effectiveness of the proposed method, we applied the deblurring network to our self-built dataset of blurred ultraviolet (UV) images of colorless chemicals floating on water surfaces. Compared to the SRN-Deblur deblurring network, the proposed approach yields improved PSNR and SSIM results. Moreover, when compared with the state-of-the-art DeblurGANv2 method, it performs better in the SSIM. These results demonstrate that the proposed multi-scale recurrent deblurring network, based on the CBAM attention mechanism, exhibits a superior deblurring performance. [ABSTRACT FROM AUTHOR]

Image production tools do not always create a clear image, noisy and blurry images are sometimes created. Among these cases, Poissonian noise is one of the most famous noises that appear in medical images and images taken in astronomy. In recent years, various methods have been proposed to improve the quality of the image that has been lost due to this noise. For example, we can refer to methods that use fractional-order and second-order total variation priors or proximal thresholding. In this paper, in the first step, based on framelet transform, a local minimal prior is introduced, and in the next step, this tool together with fractional calculation is used for Poissonian blurred image deconvolution. The framelet transform domain of images usually have sparse representations. It is also well known that the use of framelet transfer has a proper effect on the edges of the restored image. Also, In this study, both blind and nonblind problems are considered. To evaluate the performance of the presented model, several images such as real images have been investigated. Various tools are used to study the efficiency of the proposed method such as PSNR and SSIM. The proposed method is compared with the existing methods such as fractional - order and second-order total variation. The simulation results show the appropriate representation of the proposed method in solving this type of problem. [ABSTRACT FROM AUTHOR]

Synthetic aperture radar (SAR) is a remote sensing device that extracts the earth's surface's geo and biophysical characteristics. Classification performance is a major phase in SAR processing. Speckle noise occurs in SAR due to the coherent combination of backscatter signals from different sources. One of the approaches for suppressing the noise from SAR is to utilize local statistics. The proposed architecture evaluates the robustness of several improved filters like Improved Lopez, Improved Boxcar, Improved Guided filter and improved Lee-sigma and verifies their effects on classification accuracy. These filters were designed to overcome the suppression of target points and the blurring of edges. The supervised Wishart classifier with an improved Sparrow Search Algorithm (WC-ISSA) is utilized in the classification. SSA is used to optimize WC parameters and improve classification performance. One of the essential parameters in speckle noise filtering is the size of the sliding window. The window size varies, and the improved filters' performance is evaluated. Further, a growing self-organizing map (GSOM) is used to improve blurring performance. The proposed model is used for deblurring and enhancing the performance of smoothing images. The overall evaluation is carried out on the Matlab platform. The performance of the improved filters is compared to the standard filters, and the performances are compared on the virtual SAR dataset. The implemented results proved that the Extended Lee-sigma performed better than other filters. The PSNR and SSIM obtained by the proposed model were found to be 65.72 and 99.92%, respectively, which is considered to be more effective than other models already in use. [ABSTRACT FROM AUTHOR]

In this study, we proposed an image deconvolution method in the expectation maximization (EM) framework. This method involves two steps: (i) E-step: utilizing the fast Fourier transform (FFT) for computationally efficient inversion of the convolution operator and (ii) M-step: employing the discrete wavelet transform (DWT) for estimating the original image from the image obtained in the E-step. In M-step, we proposed a modified L1-clipped penalty resulting in a hybrid thresholding scheme that integrates conventional hard and soft thresholds. This hybrid threshold ameliorates the inherent bias-variance trade-offs associated with traditional hard and soft thresholding schemes. The mathematical expressions for the risk, bias, and variance of the proposed hybrid threshold are derived and the performance is evaluated through simulation. Experimental results show that the proposed method achieves optimal values for variance and bias, thereby minimizing the risk. Moreover, the proposed method outperforms state-of-the-art methods in terms of performance metrics: PSNR, ISNR, and SSIM. [ABSTRACT FROM AUTHOR]

It is known that image priors are essential to blind deconvolution. Reweighted graph total variation (RGTV), as a new prior to substitute the most classical total variation (TV), has been shown superior to TV and several other cutting-edge models both theoretically and empirically. This paper steps forward, firstly providing a simpler geometric perspective to RGTV in the framework of variational partial differential equations (PDEs), rather than the previous graph spectral interpretation made in the graph frequency domain. Surprisingly, a slight shift of perspective as such finally leads to a huge blind deblurring performance boosting in both accuracy and efficiency as compared to the previously derived numerical approach, which approximates RGTV as the graph L1-Laplacian regularizer. Utilizing the simplified RGTV as reformulated in this paper, another valuable contribution is an exploration of its potentials for blind facial image restoration by combining unsupervised deep facial models. Experimental results of blind face deblurring and blind face hallucination both demonstrate necessity and rationale of a joint model-based and learning-based approach to blind face restoration. [ABSTRACT FROM AUTHOR]

The challenge of image reconstruction from very-low-resolution images is made exceedingly difficult by multiple degradation factors in practical applications. Traditional methods do not consider the interactions between these degradation factors, so the results are often insufficient. To reconstruct low-resolution blurry images, both super-resolution and deblurring processes must be applied. In this paper, we propose a joint super-resolution and a deblurring model with integrated processing of the degradation factors to obtain better image quality. The joint model includes two branches, a super-resolution module and deblurring module, and both of them share the same feature extraction module. The super-resolution module consists of multiple layers of residual blocks. The deblurring module supports the robustness of the super-resolution module through feature feed-back in the learning process, by introducing an image blurring feature description into the feature representation. To create modules with high magnification, the base two-branch model is also used in two stages with scale recursion. A second-stage deblurring module receives the output of the first-stage super-resolution module and improves the deblurring capability when the image is further magnified. The modules enhance each other, significantly improve the quality of very-low-resolution text images, and maintain a low model complexity. A step-by-step training strategy is applied to reduce second-stage training difficulty. Experiments show that our approach significantly outperforms state-of-the-art methods in terms of image quality and optical character recognition accuracy, and with a lower computational cost. [ABSTRACT FROM AUTHOR]

Since MRI is often corrupted by Rician noise, in medical image processing, Rician denoising and deblurring is an important research. In this work, considering the validity of the non-convex log term in the Rician denoising and deblurring model estimated by the maximum a posteriori (MAP) and total variation, we apply nmBDCA to deal with the model. A non-monotonic line search applied in nmBDCA can achieve possible growth of objective function values controlled by parameters. After that, the obtained convex problem is solved separately by alternating direction method of multipliers (ADMM). For u - subproblem in ADMM scheme, Caputo fractional derivative and tailored finite point method are applied to denoising, which retain more texture details and suppress the staircase effect. We also demonstrate the convergence of the model and perform the stability analysis on the numerical scheme. Numerical results show that our method can well improve the quality of image restoration. [ABSTRACT FROM AUTHOR]

This paper presents a new accelerated proximal Markov chain Monte Carlo methodology to perform Bayesian inference in imaging inverse problems with an underlying convex geometry. The proposed strategy takes the form of a stochastic relaxed proximal-point iteration that admits two complementary interpretations. For models that are smooth or regularized by Moreau-Yosida smoothing, the algorithm is equivalent to an implicit midpoint discretization of an overdamped Langevin diffusion targeting the posterior distribution of interest. This discretization is asymptotically unbiased for Gaussian targets and shown to converge in an accelerated manner for any target that is κ-strongly log-concave (i.e., requiring in the order of √κ iterations to converge, similar to accelerated optimization schemes), comparing favorably to Pereyra, Vargas Mieles, and Zygalakis [SIAM J. Imaging Sci., 13 (2020), pp. 905-935], which is only provably accelerated for Gaussian targets and has bias. For models that are not smooth, the algorithm is equivalent to a Leimkuhler-Matthews discretization of a Langevin diffusion targeting a Moreau-Yosida approximation of the posterior distribution of interest and hence achieves a significantly lower bias than conventional unadjusted Langevin strategies based on the Euler-Maruyama discretization. For targets that are κ-strongly log-concave, the provided nonasymptotic convergence analysis also identifies the optimal time step, which maximizes the convergence speed. The proposed methodology is demonstrated through a range of experiments related to image deconvolution with Gaussian and Poisson noise with assumption-driven and data-driven convex priors. Source codes for the numerical experiments of this paper are available from https://github.com/MI2G/accelerated-langevin-imla. [ABSTRACT FROM AUTHOR]

Deconvolution is a commonly employed technique for enhancing image quality in optical imaging methods. Unfortunately, its application in optical coherence tomography (OCT) is often hindered by sensitivity to noise, which leads to additive ringing artifacts. These artifacts considerably degrade the quality of deconvolved images, thereby limiting its effectiveness in OCT imaging. In this study, we propose a framework that integrates numerical random phase masks into the deconvolution process, effectively eliminating these artifacts and enhancing image clarity. The optimized joint operation of an iterative Richardson-Lucy deconvolution and numerical synthesis of random phase masks (RPM), termed as Deconv-RPM, enables a 2.5-fold reduction in full width at half-maximum (FWHM). We demonstrate that the Deconv-RPM method significantly enhances image clarity, allowing for the discernment of previously unresolved cellular-level details in nonkeratinized epithelial cells ex vivo and moving blood cells in vivo.

Precipitation nowcasting based on artificial intelligence has garnered widespread attention in the meteorological and computer communities in recent years. While new models are continuously proposed to refresh the forecasting performance, the problem of gradual blurring of forecast maps as the forecast period extends is still serious. Most models use the mean loss and the recursive prediction structure [such as multiscale recurrent neural network (MS-RNN)]. The mean loss always results in an average of future states, visually appearing as a blur. The recursive prediction method brings the accumulation of error (blur), causing the error (blur) of long-term predictions to increase exponentially. In this study, we add the adversarial loss and gradient loss to penalize the network to ease the blur caused by the averaging loss, and we introduce an additional deblurring network (composed of MS-RNN) behind the forecasting network (composed of MS-RNN) to alleviate the blur caused by the recursive structure, which reduces the blur of the current frame and then recursively and incrementally reduces the blur of subsequent frames. We name the proposed model DB-RNN, which can slow down the error accumulation and alleviate the blurring dilemma. Like MS-RNN, DB-RNN is compatible with multiple recurrent neural network models, such as ConvLSTM, TrajGRU, PredRNN, PredRNN++, MIM, MotionRNN, PrecipLSTM, etc. Experiments on two large radar datasets named HKO-7 and DWD-12 indicate that DB-RNN's predictions are more accurate and clear than those from MS-RNN.

The quality of visual media is critically impacted by low illumination and the presence of airborne particulates, leading to challenges in brightness balance, color saturation, and texture clarity which are detrimental to various applications in image processing and computer vision. Addressing these challenges, this study introduces a novel image enhancement algorithm that significantly improves the quality of degraded images. Our proposed method, the multi-channel phase activation and multi-constraint dark channel prior (MMDCP), leverages an innovative approach by integrating the phase-adjusted Gaussian kernel function for brightness channel optimization in the Fourier transform frequency domain. This optimization is enhanced through the application of a saturated dark channel prior, achieving simultaneous brightness enhancement and color fidelity. Furthermore, we refine the dark channel prior deblurring algorithm by incorporating intensity, brightness, and color constraints to correct overexposure issues and color offsets in the reconstructed images. The efficacy of the MMDCP algorithm is demonstrated through extensive experimentation, comparing it against six contemporary image enhancement algorithms using two types of objective indicators and subjective assessments across four public datasets. The MMDCP algorithm consistently outperforms the existing methods, with a notable average improvement of 20% in PSNR and 19.6% in SSIM metrics, substantiating its superiority in enhancing brightness, detail, and color accuracy. This study's results underline the MMDCP algorithm's robustness and versatility in improving image quality across various conditions, including daytime, nighttime, indoor, and outdoor settings. [ABSTRACT FROM AUTHOR]

Purpose: Robust implementation of spiral imaging requires efficient deblurring. A deblurring method was previously proposed to separate and deblur water and fat simultaneously, based on image‐space kernel operations. The goal of this work is to improve the performance of the previous deblurring method using kernels with better properties. Methods: Four types of kernels were formed using different models for the region outside the collected k‐space as well as low‐pass preconditioning (LP). The performances of the kernels were tested and compared with both phantom and volunteer data. Data were also synthesized to evaluate the SNR. Results: The proposed "square" kernels are much more compact than the previously used circular kernels. Square kernels have better properties in terms of normalized RMS error, structural similarity index measure, and SNR. The square kernels created by LP demonstrated the best performance of artifact mitigation on phantom data. Conclusions: The sizes of the blurring kernels and thus the computational cost can be reduced by the proposed square kernels instead of the previous circular ones. Using LP may further enhance the performance. [ABSTRACT FROM AUTHOR]

Deep network design is a fundamental challenge. A right trade‐off between depth and complexity of convolutional neural networks is of significant importance to applications in low‐level vision tasks. Wider feature maps could be beneficial to performance and generality but would increase computational complexity. In this paper, we rethink the balance between width of the feature maps and depth of the network especially for image restoration tasks including deblurring, dehazing, super‐resolution, and denoising. We explore a new approach to network structure by encouraging more depth to deal with restoration requirements while decreasing the width of some feature maps. Such a slimmer and deeper approach can enhance the performance while maintaining the same level of computational costs. We have experimentally evaluated the performances of the proposed approach on four image restoration tasks and obtained state‐of‐the‐art results on quantitative measures and qualitative assessments, demonstrating the effectiveness of the approach. [ABSTRACT FROM AUTHOR]

Blurring is a significant concern in electro-optical satellite imagery due to its negative effect on image quality, which is caused by an undesirable loss of bandwidth. Blurred images and compromised image quality may result from atmospheric distortions, camera aberrations, and relative motion during the imaging process. Deblurring is thus a process used to restore deteriorated images, reduce blur, and recover the original image. The major type of blur explored in this study is Gaussian blur, and its effects on the image are investigated by applying the blur in equal proportions. Furthermore, influence of blur on the Modulation Transfer Function (MTF) of an electrooptic satellite image as well as the impacts of deblurring techniques, namely the Richardson-Lucy Deconvolution Algorithm, the Regularization Filter, and the Blind Deconvolution Algorithm on MTF values, were explored. [ABSTRACT FROM AUTHOR]

Copyright of Tikrit Journal of Engineering Sciences is the property of Republic of Iraq Ministry of Higher Education & Scientific Research (MOHESR) and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Fundus cameras are widely used by ophthalmologists for monitoring and diagnosing retinal pathologies. Unfortunately, no optical system is perfect, and the visibility of retinal images can be greatly degraded due to the presence of problematic illumination, intraocular scattering, or blurriness caused by sudden movements. To improve image quality, different retinal image restoration/enhancement techniques have been developed, which play an important role in improving the performance of various clinical and computer-assisted applications. This paper gives a comprehensive review of these restoration/enhancement techniques, discusses their underlying mathematical models, and shows how they may be effectively applied in real-life practice to increase the visual quality of retinal images for potential clinical applications including diagnosis and retinal structure recognition. All three main topics of retinal image restoration/enhancement techniques, i.e., illumination correction, dehazing, and deblurring, are addressed. Finally, some considerations about challenges and the future scope of retinal image restoration/enhancement techniques will be discussed.

Copyright of Ordnance Industry Automation is the property of Editorial Board for Ordnance Industry Automation and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Background: For static scenes with multiple depth layers, the existing defocused image deblurring methods have the problems of edge ringing artifacts or insufficient deblurring degree due to inaccurate estimation of blur amount, In addition, the prior knowledge in non blind deconvolution is not strong, which leads to image detail recovery challenge. Methods: To this end, this paper proposes a blur map estimation method for defocused images based on the gradient difference of the boundary neighborhood, which uses the gradient difference of the boundary neighborhood to accurately obtain the amount of blurring, thus preventing boundary ringing artifacts. Then, the obtained blur map is used for blur detection to determine whether the image needs to be deblurred, thereby improving the efficiency of deblurring without manual intervention and judgment. Finally, a non blind deconvolution algorithm is designed to achieve image deblurring based on the blur amount selection strategy and sparse prior. Results: Experimental results show that our method improves PSNR and SSIM by an average of 4.6% and 7.3%, respectively, compared to existing methods. Conclusions: Experimental results show that our method outperforms existing methods. Compared with existing methods, our method can better solve the problems of boundary ringing artifacts and detail information preservation in defocused image deblurring.

Purpose: Deep learning superresolution (SR) is a promising approach to reduce MRI scan time without requiring custom sequences or iterative reconstruction. Previous deep learning SR approaches have generated low‐resolution training images by simple k‐space truncation, but this does not properly model in‐plane turbo spin echo (TSE) MRI resolution degradation, which has variable T2 relaxation effects in different k‐space regions. To fill this gap, we developed a T2‐deblurred deep learning SR method for the SR of 3D‐TSE images. Methods: A SR generative adversarial network was trained using physically realistic resolution degradation (asymmetric T2 weighting of raw high‐resolution k‐space data). For comparison, we trained the same network structure on previous degradation models without TSE physics modeling. We tested all models for both retrospective and prospective SR with 3 × 3 acceleration factor (in the two phase‐encoding directions) of genetically engineered mouse embryo model TSE‐MR images. Results: The proposed method can produce high‐quality 3 × 3 SR images for a typical 500‐slice volume with 6–7 mouse embryos. Because 3 × 3 SR was performed, the image acquisition time can be reduced from 15 h to 1.7 h. Compared to previous SR methods without TSE modeling, the proposed method achieved the best quantitative imaging metrics for both retrospective and prospective evaluations and achieved the best imaging‐quality expert scores for prospective evaluation. Conclusion: The proposed T2‐deblurring method improved accuracy and image quality of deep learning–based SR of TSE MRI. This method has the potential to accelerate TSE image acquisition by a factor of up to 9. [ABSTRACT FROM AUTHOR]

Purpose: To present the validation of a new Flexible Ultra‐Short Echo time (FUSE) pulse sequence using a short‐T2 phantom. Methods: FUSE was developed to include a range of RF excitation pulses, trajectories, dimensionalities, and long‐T2 suppression techniques, enabling real‐time interchangeability of acquisition parameters. Additionally, we developed an improved 3D deblurring algorithm to correct for off‐resonance artifacts. Several experiments were conducted to validate the efficacy of FUSE, by comparing different approaches for off‐resonance artifact correction, variations in RF pulse and trajectory combinations, and long‐T2 suppression techniques. All scans were performed on a 3 T system using an in‐house short‐T2 phantom. The evaluation of results included qualitative comparisons and quantitative assessments of the SNR and contrast‐to‐noise ratio. Results: Using the capabilities of FUSE, we demonstrated that we could combine a shorter readout duration with our improved deblurring algorithm to effectively reduce off‐resonance artifacts. Among the different RF and trajectory combinations, the spiral trajectory with the regular half‐inc pulse achieves the highest SNRs. The dual‐echo subtraction technique delivers better short‐T2 contrast and superior suppression of water and agar signals, whereas the off‐resonance saturation method successfully suppresses water and lipid signals simultaneously. Conclusion: In this work, we have validated the use of our new FUSE sequence using a short T2 phantom, demonstrating that multiple UTE acquisitions can be achieved within a single sequence. This new sequence may be useful for acquiring improved UTE images and the development of UTE imaging protocols. [ABSTRACT FROM AUTHOR]

Purpose: The combination of SENSE and spiral imaging with fat/water separation enables high temporal efficiency. However, the corresponding computation increases due to the blurring/deblurring operation across the multi‐channel data. This study presents two alternative models to simplify computational complexity in the original full model (model 1). The performances of the models are evaluated in terms of the computation time and reconstruction error. Methods: Two approximated spiral MRI reconstruction models were proposed: the comprehensive blurring before coil operation (model 2) and the regional blurring before coil operation (model 3), respectively, by altering the order of coil‐sensitivity encoding process to distribute signals among the multi‐channel coils. Four subjects were recruited for scanning both fully sampled T1‐ and T2‐weighted brain image data with simulated undersampling for testing the computational efficiency and accuracy on the approximation models. Results: Based on the examples, the computation time can be reduced to 31%–47% using model 2, and to 39%–56% using model 3. The quality of the water image remains unchanged among the three models, whereas the primary difference in image quality is in the fat channel. The fat images from model 3 are consistent with those from model 1, but those from model 2 have higher normalized error, differing by up to 4.8%. Conclusion: Model 2 provides the fastest computation but exhibits higher error in the fat channel, particularly in the high field and with long acquisition window. Model 3, an abridged alternative, is also faster than the full model and can maintain high accuracy in reconstruction. [ABSTRACT FROM AUTHOR]

Ultralow‐field (ULF) magnetic resonance imaging (MRI) can suffer from inferior image quality because of low signal‐to‐noise ratio (SNR). As an efficient way to cover the k‐space, the spiral acquisition technique has shown great potential in improving imaging SNR efficiency at ULF. The current study aimed to address the problems of noise and blurring cancelation in the ULF case with spiral trajectory, and we proposed a spiral‐out sequence for brain imaging using a portable 50‐mT MRI system. The proposed sequence consisted of three modules: noise calibration, field map acquisition, and imaging. In the calibration step, transfer coefficients were obtained between signals from primary and noise‐pick‐up coils to perform electromagnetic interference (EMI) cancelation. Embedded field map acquisition was performed to correct accumulated phase error due to main field inhomogeneity. Considering imaging SNR, a lower bandwidth for data sampling was adopted in the sequence design because the 50‐mT scanner is in a low SNR regime. Image reconstruction proceeded with sampled data by leveraging system imperfections, such as gradient delays and concomitant fields. The proposed method can provide images with higher SNR efficiency compared with its Cartesian counterparts. An improvement in temporal SNR of approximately 23%–44% was measured via phantom and in vivo experiments. Distortion‐free images with a noise suppression rate of nearly 80% were obtained by the proposed technique. A comparison was also made with a state‐of‐the‐art EMI cancelation algorithm used in the ULF‐MRI system. SNR efficiency‐enhanced spiral acquisitions were investigated for ULF‐MR scanners and future studies could focus on various image contrasts based on our proposed approach to widen ULF applications. [ABSTRACT FROM AUTHOR]

Abstract: Image deblurring is a common issue in low-level computer vision aiming to restore a clear image from a blurred input image. Deep learning innovations have significantly advanced the solution to this issue, and numerous deblurring networks have been presented to recover high-quality images. This study aims to investigate the impact of Blind deconvolution and Non-Blind Deconvolution (Weiner Filter, Regularized Filter, and lucky Richardson) deblurring techniques and blind deconvolution to retrieve the original image from the blurring and the noisy images. Point Spread Function (PSF) is required to perform the deconvolution process. MATLAB program is utilized in this study as a suitable tool for image processing. Peak to Signal Ratio (PSNR) and structural index similarity (SSIM) are the major parameters used to examine image quality. The results showed that the Regularized Filter was an effective technique to deblur the blurry image, and it achieved the largest PSNR and best SSIM with the prior information about the PSF for different degrees of blurring angle. These four deblurring techniques were unsuccessful in restoring the original image from the image with Gaussian noise.

Abstract Background Efficient and site-specific weed management is a critical step in many agricultural tasks. Image captures from drones and modern machine learning based computer vision methods can be used to assess weed infestation in agricultural fields more efficiently. However, the image quality of the captures can be affected by several factors, including motion blur. Image captures can be blurred because the drone moves during the image capturing process, e.g. due to wind pressure or camera settings. These influences complicate the annotation of training and test samples and can also lead to reduced predictive power in segmentation and classification tasks. Results In this study, we propose DeBlurWeedSeg, a combined deblurring and segmentation model for weed and crop segmentation in motion blurred images. For this purpose, we first collected a new dataset of matching sharp and naturally blurred image pairs of real sorghum and weed plants from drone images of the same agricultural field. The data was used to train and evaluate the performance of DeBlurWeedSeg on both sharp and blurred images of a hold-out test-set. We show that DeBlurWeedSeg outperforms a standard segmentation model that does not include an integrated deblurring step, with a relative improvement of $$13.4 \%$$ 13.4 % in terms of the Sørensen-Dice coefficient. Conclusion Our combined deblurring and segmentation model DeBlurWeedSeg is able to accurately segment weeds from sorghum and background, in both sharp as well as motion blurred drone captures. This has high practical implications, as lower error rates in weed and crop segmentation could lead to better weed control, e.g. when using robots for mechanical weed removal.

Building occupancy information is significant for a variety of reasons, from allocation of resources in smart buildings to responding during emergency situations. As most people spend more than 90% of their time indoors, a comfortable indoor environment is crucial. To ensure comfort, traditional HVAC systems condition rooms assuming maximum occupancy, accounting for more than 50% of buildings’ energy budgets in the US. Occupancy level is a key factor in ensuring energy efficiency, as occupancy-controlled HVAC systems can reduce energy waste by conditioning rooms based on actual usage. Numerous studies have focused on developing occupancy estimation models leveraging existing sensors, with camera-based methods gaining popularity due to their high precision and widespread availability. However, the main concern with using cameras for occupancy estimation is the potential violation of occupants’ privacy. Unlike previous video-/image-based occupancy estimation methods, we addressed the issue of occupants’ privacy in this work by proposing and investigating both motion-based and motion-independent occupancy counting methods on intentionally blurred video frames. Our proposed approach included the development of a motion-based technique that inherently preserves privacy, as well as motion-independent techniques such as detection-based and density-estimation-based methods. To improve the accuracy of the motion-independent approaches, we utilized deblurring methods: an iterative statistical technique and a deep-learning-based method. Furthermore, we conducted an analysis of the privacy implications of our motion-independent occupancy counting system by comparing the original, blurred, and deblurred frames using different image quality assessment metrics. This analysis provided insights into the trade-off between occupancy estimation accuracy and the preservation of occupants’ visual privacy. The combination of iterative statistical deblurring and density estimation achieved a 16.29% counting error, outperforming our other proposed approaches while preserving occupants’ visual privacy to a certain extent. Our multifaceted approach aims to contribute to the field of occupancy estimation by proposing a solution that seeks to balance the trade-off between accuracy and privacy. While further research is needed to fully address this complex issue, our work provides insights and a step towards a more privacy-aware occupancy estimation system.

Automatic imaging monitoring technology has been developed as a mature solution to address oil spill accidents. However, when airborne equipment is used to monitor the area of leakage, there is a chance of relative motion between the equipment and the target, leading to blurred images. This can hinder subsequent monitoring tasks, such as the identification and segmentation of hazardous chemical targets. Therefore, it is crucial to deblur these images, as sharper images can substantially improve the precision and accuracy of these tasks. In light of this, this paper presents a novel multi-scale recurrent deblurring network, leveraging the Convolutional Block Attention Module (CBAM) attention mechanism, to restore clear images in an end-to-end manner. The CBAM attention mechanism has been incorporated, which learns features in both the spatial and channel domains, thereby enhancing the deblurring ability of the network by combining the attention mechanisms of multiple domains. To evaluate the effectiveness of the proposed method, we applied the deblurring network to our self-built dataset of blurred ultraviolet (UV) images of colorless chemicals floating on water surfaces. Compared to the SRN-Deblur deblurring network, the proposed approach yields improved PSNR and SSIM results. Moreover, when compared with the state-of-the-art DeblurGANv2 method, it performs better in the SSIM. These results demonstrate that the proposed multi-scale recurrent deblurring network, based on the CBAM attention mechanism, exhibits a superior deblurring performance.

A computational and experimental study is conducted to examine how directivity associated with a finite aperture sensor affects photoacoustic tomography (PAT) image reconstruction. Acoustic signals for the simulation work were computed using a discrete particle approach from three numerical phantoms including a vasculature. The theoretical framework and a Monte Carlo approach for construction of a tissue configuration are discussed in detail. While simulating forward data, the directivity of the sensor was taken into account. The image reconstruction was accomplished using system matrix based methods like l2 norm Tikhonov regularization, l1 norm regularization and total variation (TV) minimization. Accordingly, two different system matrices were constructed- (i) assuming transducer as a point detector (PD) and (ii) retaining properties of a finite detector with directivity (FDWD). Image reconstruction was also performed utilizing experimentally measured PA signals. Both the computational and experimental results demonstrate that blur-free PAT imaging can be achieved with the FDWD method. Additionally, TV minimization provides marginally better image reconstruction compared to the other schemes.

Background: Efficient and site-specific weed management is a critical step in many agricultural tasks. Image captures from drones and modern machine learning based computer vision methods can be used to assess weed infestation in agricultural fields more efficiently. However, the image quality of the captures can be affected by several factors, including motion blur. Image captures can be blurred because the drone moves during the image capturing process, e.g. due to wind pressure or camera settings. These influences complicate the annotation of training and test samples and can also lead to reduced predictive power in segmentation and classification tasks. Results: In this study, we propose DeBlurWeedSeg, a combined deblurring and segmentation model for weed and crop segmentation in motion blurred images. For this purpose, we first collected a new dataset of matching sharp and naturally blurred image pairs of real sorghum and weed plants from drone images of the same agricultural field. The data was used to train and evaluate the performance of DeBlurWeedSeg on both sharp and blurred images of a hold-out test-set. We show that DeBlurWeedSeg outperforms a standard segmentation model that does not include an integrated deblurring step, with a relative improvement of 13.4 % in terms of the Sørensen-Dice coefficient. Conclusion: Our combined deblurring and segmentation model DeBlurWeedSeg is able to accurately segment weeds from sorghum and background, in both sharp as well as motion blurred drone captures. This has high practical implications, as lower error rates in weed and crop segmentation could lead to better weed control, e.g. when using robots for mechanical weed removal. [ABSTRACT FROM AUTHOR]

In microscopic vision, the sharpness of edge profile seriously affects the accuracy of measurement. However, various factors in complex environments often blur the edge profile. Herein, a CNN-related deblurring algorithm is proposed to reconstruct the real edge features from the blurred microscopic images. Based on the convolutional neural network, a network called I&R Net is built up, in which both the inception block and the residual channel attention block are integrated. Moreover, the loss functions are constructed with the additional factor of gradient by Sobel operator and Laplacian operator to enhance the inductive bias of I&R Net on real size. The results show that the I&R Net improves both the general image quality and the accuracy. Additionally, the I&R Net presents relatively strong resistance to the influence of feature-rotating. • An integrated deblurring model. • Loss functions with the additional factor of gradient. • Resistance to rotating influence. [ABSTRACT FROM AUTHOR]

• An advanced Reference-Constrained Image Restoration algorithm is proposed for solving the image restoration task. • Our algorithm takes advantage of image prior information and merges it with Regularization by Denoising. • By using multiple reference images, we increase the possibility to improve the quality of the reconstructed images. • The achievable performance of the proposed algorithm outperforms Regularization by Denoising. Image restoration is always a long-term problem in low-level computer vision, which has many practical applications. In this paper, we devote to reconstructing a single image from compressive measurements with the assistance of one or multiple reference images, by using the similarity prior information of them. More specifically, we propose to bring in the concept of Regularization by Denoising, which leverages existing denoising engine to regularize inverse problems, and illustrate how the reference images and this powerful algorithm can be merged into a new highly effective recovery approach, which is named Reference Image Constrained Regularization by Denoising for solving various image restoration tasks. Since the similarity between the reference and the ground truth is not guaranteed, or the degree of the similarity is different, we extend the proposed algorithm by adding adaptive weighting matrix to the difference between the target image and the reference images to further improve the performance. We evaluate experimentally the proposed algorithm in the image deblurring and single image super-resolution problems. Furthermore, we also carry out our method on video super-resolution task, in the case of multiple reference images available. We compare the advanced reference-constrained image restoration algorithm with previous methods about reconstruction accuracy and computation cost. The experimental results show that the proposed method outperforms state-of-the-art algorithms, including Regularization by Denoising, in terms of both the Peak Signal to Noise Ratio and the visual performance. [ABSTRACT FROM AUTHOR]

Motivated by applications in image processing, we consider a class of structured convex optimization problems in which the objective function is the sum of two component functions with favorable structures, and, furthermore, both of which are composed with linear operators. In particular, to recover images degraded by Poisson noise, the famous Kullback-Leibler divergence can be adopted in data fitting, in which case one component function is separable, strongly convex and easy to minimize. The other component function usually allows an easily computable proximal operator. We then propose, analyze and test a hybrid algorithm particularly tailored for the underlying model structures, which can be viewed as a combination of the famous alternating minimization algorithm of Tseng and the alternating direction method of multipliers. Under mild conditions, global iterate convergence as well as ergodic $ \mathcal{O}(1/N) $ sublinear convergence rate results measured by the function value residual and constraint violation of a reformulated constrained optimization problem are established, where $ N $ denotes the iteration counter. The proposed algorithm is applied to fractional-order total variation regularized image denoising and deblurring problems, where the noise obeys Poisson distribution. Our experimental results demonstrate the effectiveness of the proposed algorithm. [ABSTRACT FROM AUTHOR]

Conventional inversion of the discrete Fourier transform (DFT) requires all DFT coefficients to be known. When the DFT coefficients of a rasterized image (represented as a matrix) are known only within a pass band, the original matrix cannot be uniquely recovered. In many cases of practical importance, the matrix is binary and its elements can be reduced to either 0 or 1. This is the case, for example, for the commonly used QR codes. The a priori information that the matrix is binary can compensate for the missing high-frequency DFT coefficients and restore uniqueness of image recovery. This paper addresses, both theoretically and numerically, the problem of recovery of blurred images without any known structure whose high-frequency DFT coefficients have been irreversibly lost by utilizing the binarity constraint. We investigate theoretically the smallest band limit for which unique recovery of a generic binary matrix is still possible. Uniqueness results are proved for images of sizes N1×N2, N1×N1, and N\α 1×Nα 1, where N¹\not =N2 are prime numbers and\alpha >1 an integer. Inversion algorithms are proposed for recovering the matrix from its band-limited (blurred) version. The algorithms combine integer linear programming methods with lattice basis reduction techniques and significantly outperform naive implementations. The algorithm efficiently and reliably reconstructs severely blurred 29×29 binary matrices with only 11×11 = 121 DFT coefficients. [ABSTRACT FROM AUTHOR]