Search

Using multi-view images of forest plots to reconstruct dense point clouds and extract individual tree parameters enables rapid, high-precision, and cost-effective forest plot surveys. However, images captured at close range face challenges in forest reconstruction, such as unclear canopy reconstruction, prolonged reconstruction times, insufficient accuracy, and issues with tree duplication. To address these challenges, this paper introduces a new image dataset creation process that enhances both the efficiency and quality of image acquisition. Additionally, a block-matching-based multi-view reconstruction algorithm, Forest Multi-View Reconstruction with Enhanced Confidence-Guided Dynamic Domain Propagation (CDP-MVS), is proposed. The CDP-MVS algorithm addresses the issue of canopy and sky mixing in reconstructed point clouds by segmenting the sky in the depth maps and setting its depth value to zero. Furthermore, the algorithm introduces a confidence calculation method that comprehensively evaluates multiple aspects. Moreover, CDP-MVS employs a decentralized dynamic domain propagation sampling strategy, guiding the propagation of the dynamic domain through newly defined confidence measures. Finally, this paper compares the reconstruction results and individual tree parameters of the CDP-MVS, ACMMP, and PatchMatchNet algorithms using self-collected data. Visualization results show that, compared to the other two algorithms, CDP-MVS produces the least sky noise in tree reconstructions, with the clearest and most detailed canopy branches and trunk sections. In terms of parameter metrics, CDP-MVS achieved 100% accuracy in reconstructing tree quantities across the four plots, effectively avoiding tree duplication. The accuracy of breast diameter extraction values of point clouds reconstructed by CDPMVS reached 96.27%, 90%, 90.64%, and 93.62%, respectively, in the four sample plots. The positional deviation of reconstructed trees, compared to ACMMP, was reduced by 0.37 m, 0.07 m, 0.18 m and 0.33 m, with the average distance deviation across the four plots converging within 0.25 m. In terms of reconstruction efficiency, CDP-MVS completed the reconstruction of the four plots in 1.8 to 3.1 h, reducing the average reconstruction time per plot by six minutes compared to ACMMP and by two to three times compared to PatchMatchNet. Finally, the differences in tree height accuracy among the point clouds reconstructed by the different algorithms were minimal. The experimental results demonstrate that CDP-MVS, as a multi-view reconstruction algorithm tailored for forest reconstruction, shows promising application potential and can provide valuable support for forestry surveys. [ABSTRACT FROM AUTHOR]

Ultrasound speckle tracking enables in vivo measurement of soft tissue deformation or strain, providing a non-invasive diagnostic tool to quantify tissue health. However, adoption into new fields is challenging since algorithms need to be tuned with gold-standard reference data that are expensive or impractical to acquire. Here, we present a novel optimization approach that only requires repeated measurements, which can be acquired for new applications where reference data might not be readily available or difficult to get hold of. Soft tissue motion was captured using ultrasound for the medial collateral ligament (MCL) of three quasi-statically loaded porcine stifle joints, and medial ligamentous structures of a dynamically loaded human cadaveric knee joint. Using a training subset, custom speckle tracking algorithms were created for the porcine and human ligaments using surrogate optimization, which aimed to maximize repeatability by minimizing the normalized standard deviation of calculated strain maps for repeat measurements. An unseen test subset was then used to validate the tuned algorithms by comparing the ultrasound strains to digital image correlation (DIC) surface strains (porcine specimens) and length change values of the optically tracked ligament attachments (human specimens). After 1500 iterations, the optimization routine based on the porcine and human training data converged to similar values of normalized standard deviations of repeat strain maps (porcine: 0.19, human: 0.26). Ultrasound strains calculated for the independent test sets using the tuned algorithms closely matched the DIC measurements for the porcine quasi-static measurements (R > 0.99, RMSE < 0.59%) and the length change between the tracked ligament attachments for the dynamic human dataset (RMSE < 6.28%). Furthermore, strains in the medial ligamentous structures of the human specimen during flexion showed a strong correlation with anterior/posterior position on the ligaments (R > 0.91). Adjusting ultrasound speckle tracking algorithms using an optimization routine based on repeatability led to robust and reliable results with low RMSE for the medial ligamentous structures of the knee. This tool may be equally beneficial in other soft-tissue displacement or strain measurement applications and can assist in the development of novel ultrasonic diagnostic tools to assess soft tissue biomechanics. [ABSTRACT FROM AUTHOR]

Aiming at the flicker problem faced by vehicle streaming media rearview mirror under LED signal light environment, meanwhile, considering the impact of rapid local image motion and complex edge textures on image restoration, a real-time flicker mitigation system based on FPGA(Field Programmable Gate Array) is proposed in this paper. Firstly, the constraint conditions of the flicker pixels are established according to the features of flicker images such that the flicker region are efficiently detected through three consecutive frames. Then, a motion estimation algorithm based on neighborhood images groups is proposed, the image matching criterion is improved, and the neighborhood images vectors is clustered to enhance image inpainting effect in complex scene. Finally, a parallel pipeline processing architecture for video data streams is designed based on the ZYNQ(ZYNQ-7000 All Programmable SoC) platform to achieve system acceleration. The experimental results show that the proposed system can satisfy the real-time processing requirement of 800x480 resolution and 30 fps video, and has good mitigation effect on flickering images in complex scenes. [ABSTRACT FROM AUTHOR]

In this work, the image block matching based on dissimilarity measure is investigated. Moreover, an unsupervised approach is implemented to yield that the algorithms have low complexity (in numbers of operations) compared to the full search algorithm. The state-of-the-art experiments only use discrete cosine transform as a domain transform. In addition, some images were tested to evaluate the algorithms. However, these images were not evaluated according to specific characteristics. So, in this paper, an improved version is presented to tackle the problem of dissimilarity measure in block matching with a noisy environment, using another’s domain transforms or low-pass filters to obtain a better result in block matching implementing a quantitive measure with an average accuracy margin of ± 0.05 is obtained. The theoretical analysis indicates that the complexity of these algorithms is still accurate, so implementing Hadamard spectral coefficients and Fourier filters can easily be adjusted to obtain a better accuracy of the matched block group. [ABSTRACT FROM AUTHOR]

Speckle filtering is a key preprocessing approach for the applications of polarimetric synthetic aperture radar (PolSAR) data. Most current PolSAR filtering algorithms can hardly get good balance in suppressing speckle and retaining image details. In this article, an algorithm for fully PolSAR image filtering is proposed by embedding the idea of the advanced block matching theory into the classical Lee filter, which ensures that image details can be well preserved after the significant reduction of speckle. First, taking each image block as the reference, a block matching step is taken to group those similar blocks in the nonlocal area; the Lee filter is employed to simultaneously process the center pixel of each block in the group, followed by an aggregation step which estimates the same pixel been grouped in different groups; then, the block matching step is taken again to regroup similar blocks by using the information of both the original image and the image filtered in last stage; again, the Lee filter is collaborated with the aggregation step in filtering the image. Despeckling experiments demonstrate that the proposed PolSAR filter can not only significantly suppress the image noise but also well preserve edges and textures.

Using multi-view images of forest plots to reconstruct dense point clouds and extract individual tree parameters enables rapid, high-precision, and cost-effective forest plot surveys. However, images captured at close range face challenges in forest reconstruction, such as unclear canopy reconstruction, prolonged reconstruction times, insufficient accuracy, and issues with tree duplication. To address these challenges, this paper introduces a new image dataset creation process that enhances both the efficiency and quality of image acquisition. Additionally, a block-matching-based multi-view reconstruction algorithm, Forest Multi-View Reconstruction with Enhanced Confidence-Guided Dynamic Domain Propagation (CDP-MVS), is proposed. The CDP-MVS algorithm addresses the issue of canopy and sky mixing in reconstructed point clouds by segmenting the sky in the depth maps and setting its depth value to zero. Furthermore, the algorithm introduces a confidence calculation method that comprehensively evaluates multiple aspects. Moreover, CDP-MVS employs a decentralized dynamic domain propagation sampling strategy, guiding the propagation of the dynamic domain through newly defined confidence measures. Finally, this paper compares the reconstruction results and individual tree parameters of the CDP-MVS, ACMMP, and PatchMatchNet algorithms using self-collected data. Visualization results show that, compared to the other two algorithms, CDP-MVS produces the least sky noise in tree reconstructions, with the clearest and most detailed canopy branches and trunk sections. In terms of parameter metrics, CDP-MVS achieved 100% accuracy in reconstructing tree quantities across the four plots, effectively avoiding tree duplication. The accuracy of breast diameter extraction values of point clouds reconstructed by CDPMVS reached 96.27%, 90%, 90.64%, and 93.62%, respectively, in the four sample plots. The positional deviation of reconstructed trees, compared to ACMMP, was reduced by 0.37 m, 0.07 m, 0.18 m and 0.33 m, with the average distance deviation across the four plots converging within 0.25 m. In terms of reconstruction efficiency, CDP-MVS completed the reconstruction of the four plots in 1.8 to 3.1 h, reducing the average reconstruction time per plot by six minutes compared to ACMMP and by two to three times compared to PatchMatchNet. Finally, the differences in tree height accuracy among the point clouds reconstructed by the different algorithms were minimal. The experimental results demonstrate that CDP-MVS, as a multi-view reconstruction algorithm tailored for forest reconstruction, shows promising application potential and can provide valuable support for forestry surveys.

Block matching serves as the foundation for ultrasound velocimetry techniques such as blood speckle tracking and echo-particle image velocimetry. Any spatial velocity gradients (SVGs) inside a block-matching pair will result in tracking error, due to both the finite block size and the ultrasound point-spread-function. We assess, using an in silico sinusoidal flow phantom, the effect of SVG magnitude and beam-to-flow angle on block-matching bias and precision. Secondarily we assess the effect that SVGs have on velocimetry bias when using angled plane-wave compounding. The magnitude and angle of SVGs were varied by adjusting the wavelength and direction of a sinusoidal flow profile. Scatterers displaced by this flow profile were used for simulating ultrasound radio frequency data at discrete time points. After beamforming, the 2-D flow field was estimated using block matching. Two imaging sequences were tested, a single plane-wave and a three-angled plane-wave. Smaller sinusoidal flow wavelengths resulted in increased bias and reduced precision, revealing an inverse relationship between sinusoidal flow wavelength and tracking error, with median errors ranging from 69%–90% for the smallest flow wavelengths (highest SVGs) down to 3%–5% for the largest (lowest SVGs). The SVG angle was also important, in which lateral SVGs (with axially oriented flows) resulted in significant speckle decorrelation and high tracking errors in regions with high SVGs. Conversely, axial SVGs (laterally oriented flow) experienced higher bias in the peak velocity regions of the flow profile. Coherent compounding resulted in higher velocity errors than using a single transmission for lateral SVGs but not for axial SVGs. The highest SVGs that could be measured with ≤10% error was when the sinusoidal flow wavelength was less than 20 times the ultrasound pulse wavelength. The clinical significance is that the high SVGs present in high kinetic energy flows, such as severe carotid stenosis and aortic regurgitation, will limit the ability to accurately quantify the velocities in these flow structures. [ABSTRACT FROM AUTHOR]

With the rapid development of deep learning, convolutional neural networks have gradually become the main means to extract features of dynamic image sequences. The motion vector estimation algorithm, as the key to the stability of image sequences, directly affects the performance of image stabilization systems, so the motion estimation algorithm for convolutional neural networks is necessary. The study proposes an improved convolutional neural network based on loss-free function, and applies it to the extraction of dynamic image features. On this basis, the motion estimation algorithm is then optimised by combining grey-scale projection and block matching methods. The experimental results show that the new loss-free function-based convolutional neural network has better recognition capability with an error rate of only 15% in dynamic image recognition. The accuracy of the optimised motion estimation algorithm is as high as 95.1% with a PSNR value of 16.636, which is higher than that of the traditional grey-scale projection algorithm. In terms of video processing, the improved algorithm has a higher PSNR value than the search block matching method, the bit-plane matching method and the full search block matching method, with a higher steady image accuracy and high operational efficiency, providing a new research idea for the improvement of motion estimation algorithms. In general, the proposed algorithm is a significant improvement over the current mainstream algorithms in terms of image accuracy, processing performance and number of operations, and it provides a new research idea for the improvement of motion estimation algorithms. [ABSTRACT FROM AUTHOR]

Seismic signals are inevitably disturbed by random noise in the acquisition process, which greatly degrades seismic data. In order to improve the quality of seismic data, we propose a self-similarity convolutional neural network (SS-Net) for seismic data denoising by introducing the coherence of seismic events into convolutional neural network (CNN). The SS-Net consists of two modules, the directional matching module (DMM) and the denoising module. The DMM stacks similar seismic data blocks to generate three-dimensional (3D) groups by calculating the similarity between seismic data blocks with the same directional characteristics. For the 3D groups with redundant structural information, the following denoising module with the multi-channel convolution adaptively extracts and squeezes the structural feature characteristic of each 3D group, which enhances the characteristics of seismic signals and avoids confusion caused by local similarity of seismic signals and random noise. In addition, the skip connection is adopted by SS-Net to transport the sparse feature to the following denoising process, to reduce the loss of signal features extracted by multi-channel convolutional layers due to increased network depth. We validate the denoising performance of the SS-Net on the synthetic and field desert seismic data. The filtered results confirm that the SS-Net can suppress seismic random noise more thoroughly and recover the seismic events with complex morphology better than the competitive denoising methods. [ABSTRACT FROM AUTHOR]

Image block matching is one of the representative methods for image detection and motion compensation in MPEG. Block matching between two images is a problem of finding symmetry between two images by matching macro blocks that are symmetrical to each other in two given images. The greater the PSNR value, the greater the symmetry of the two images. In the given two images, the two macro blocks with the minimum matching error values are regarded as symmetrical to each other. The classical method of calculating the matching error function for every pixel in the entire search area and choosing the smallest of them guarantees global convergence but requires a lot of computation, especially for large intensities. For this reason, many sparse search methods have been developed to reduce the amount of computation. In this paper, we introduce a gradient descent vector optimization algorithm with guaranteed global convergence to the image block matching problem by utilizing the conceptual symmetry of the vector optimization problem, which is a continuous variable, and the image block matching problem, which is a discrete variable. By blurring the image, we transform the matching cost function closer to being unimodal so that the descent-type algorithm works well. As a result, although the proposed method is simple, it can reduce the amount of computation remarkably and has more robustness for the large displacement of image blocks compared to existing sparse search methods. [ABSTRACT FROM AUTHOR]

Simultaneous source acquisition can reduce survey time and expense. Deblending is a typical method for processing simultaneous source seismic data, which is achieved by removing as much blending noise as feasible while maintaining as much useful signal as possible. We propose a novel deblending method with the shaping regularization-based iterative framework in the sparse 3-D transform domain in this letter. The core concept behind this method is to process the 2-D blended data block by block. Using the block-matching technique, we can extract similar blocks from the pseudo-deblended data and then put these 2-D blocks together to make 3-D arrays. The arrays are then transformed to the sparse 3-D transform domain to eliminate blending noise. Due to the correlation between similar blocks, the signal can be represented with greater sparsity in the 3-D transform domain than in the conventional 2-D transform domain. It is this sparsity that threshold shrinkage can be particularly successful at attenuating blending noise while retaining the characteristics of useful signals. To get the final deblended seismic data, the overlapping deblended blocks are restored to their original places and combined using a specific averaging procedure. To verify the practicability of our method, we test it with noisy blended data. The experimental results indicate that our approach can effectively remove blending and white Gaussian noise. [ABSTRACT FROM AUTHOR]

Due to the strong correlation between symmetric frames, video signals have a high degree of temporal redundancy. Motion estimation techniques are computationally expensive and time-consuming processes used in symmetric video compression to reduce temporal redundancy. The block-matching technique is, on the other hand, the most popular and efficient of the different motion estimation and compensation techniques. Motion compensation based on the block-matching technique generally uses the minimization of either the mean square error (MSE) or mean absolute difference (MAD) in order to find the appropriate motion vector. This paper proposes to remove the highly temporally redundant information contained in each block of the video signal using the removing temporal redundancy (RTR) technique in order to improve the data rate and efficiency of the video signal. A comparison between the PSNR values of this technique and those of the JPEG video compression standard is made. As a result of its moderate memory and computation requirements, the algorithm was found to be suitable for mobile networks and embedded devices. Based on a detailed set of testing scenarios and the obtained results, it is evident that the RTR compression technique allowed a compression ratio of 22.71 and 95% loss in bit rate reduction while maintaining sufficient intact signal quality with minimized information loss. [ABSTRACT FROM AUTHOR]

Quantification of the dynamics of the carotid artery wall is useful in evaluating arteriosclerosis and atherosclerosis. As the carotid artery wall moves not only in the radial direction but also in the longitudinal direction, longitudinal movement should be considered in the analysis of the dynamic properties of the carotid artery wall. In this study, we propose a "lateral M-mode" method for visualizing the longitudinal movement of the intima–media complex (IMC). For the lateral M-mode, we set the target line in the longitudinal direction along the IMC and visualize the signals on the target line frame-by-frame by correcting the position of the target line along the radial displacement estimated by the phased tracking method. Differentiating the envelope signals between consecutive ultrasound beams was effective in visualizing the lateral movement of the IMC. The precision of the longitudinal displacement of the IMC estimated using the conventional block-matching method was validated by comparing it with the lateral M-mode. Because the conventional M-mode sequence plays an important role in evaluation of the dynamics of various tissues, the proposed "lateral M-mode" contributes to a detailed understanding of vascular dynamics and the development of diagnostic methods for vascular diseases. [ABSTRACT FROM AUTHOR]

Recently, Internet of the things (IoTs) devices have been used highly in surveillance applications in smart environments, cities, and buildings. Most of the surveillance data recorded by the IoT devices is in the form of video and needs to be transmitted to the admin or server by video streaming technology. Fractal video compression uses a self-similarity concept for video compression, meaning the fractal image contains self-similarity of itself that is explained and denoted by change. Computational cost is more in fractal video coding; different methods have been developed to reduce this computational cost. The proposed work's main objective is to implement a video streaming approach by combining the Diamond-search-pattern- block-matching motion estimation algorithm and hash-based fractal video compression algorithm to reduce the encoding time. It improves both the delay and security of the block matching system and the developed approach measures based on evaluation parameters: Peak signal-to-noise ratio (PSNR), encoding time, decoding time, Mean squared error (MSE), and Compression ratio for Smart City Applications. Compared to other algorithms, the IoTs video data is streamed efficiently in a smart city to analyse the abnormalities.

The generation of Digital Elevation Models (DEMs) through stereogrammetry of optical satellite images has gained great popularity across various disciplines. For the analysis of these DEMs, it is important to understand the influence of the input data and different processing steps and parameters employed during stereo correlation. Here, we explore the effects that image texture, as well as the use of different matching algorithms (Block Matching (BM) and More Global Matching (MGM)), can have on optical DEMs derived from the flexible, open-source Ames Stereo Pipeline. Our analysis relies on a ∼2700 km 2 clip of a SPOT6 tristereo scene covering the hyperarid, vegetation-free Pocitos Basin and adjacent mountain ranges in the northwestern Argentine Andes. A large, perfectly flat salt pan (paleolake bed) that covers the center of this basin is characterized by strong contrasts in image texture, providing a unique opportunity to quantitatively study the relationship between image texture and DEM quality unaffected by topography. Our findings suggest that higher image texture, measured by panchromatic variance, leads to lower DEM uncertainty. This improvement continues up to ∼10 3 panchromatic variance, above which further improvements in DEM quality are independent of local image texture but instead may have sensor or geometric origins. Based on this behavior, we propose that image texture may serve as an important proxy of DEM quality prior to stereo correlation and can help to set adequate processing parameters. With respect to matching algorithms, we observe that MGM improves matching in low-texture areas and overall generates a smoother surface that still preserves complex, narrow (i.e., ridge and valley) features. Based on this sharper representation of the landscape, we conclude that MGM should be preferred for geomorphic applications relying on stereo-derived DEMs. However, we note that the correlation kernel selected for stereo-matching must be carefully chosen depending on local image texture, whereby larger kernels generate more accurate matches (less artifacts) at the cost of smoothing results. Overall, our analysis suggests a path forward for the processing and fusion of overlapping satellite images with suitable view-angle differences to improve final DEMs. [ABSTRACT FROM AUTHOR]

With the development of synthetic aperture radar (SAR) technology, SAR images are being widely applied, such as in SAR image fusion and transformation detection, where image matching is a key procedure. In this paper, a practical and rapid subpixel matching method is proposed for spaceborne SAR images. The subpixel SAR-oriented features from an accelerated segment test (SAR-OFAST) detection operator is employed to improve the detection accuracy of keypoints. By determining the overlapping area of master--slave images, the search range of matching is reduced, and the strategy of block matching is adopted to save computing resources. To verify the accuracy and reliability of the proposed matching method, several groups of SAR images obtained with an identical observation orientation and diverse observation angles were tested using the proposed and other methods. Experimental results demonstrate that the proposed method has a higher speed than other methods with similar performance in other indicators, making it more suitable for scenarios requiring high efficiency. [ABSTRACT FROM AUTHOR]

Purpose. Cherenkov imaging during radiotherapy provides a real time visualization of beam delivery on patient tissue, which can be used dynamically for incident detection or to review a summary of the delivered surface signal for treatment verification. Very few photons form the images, and one limitation is that the noise level per frame can be quite high, and mottle in the cumulative processed images can cause mild overall noise. This work focused on removing or suppressing noise via image postprocessing. Approach. Images were analyzed for peak-signal-to-noise and spatial frequencies present, and several established noise/mottle reduction algorithms were chosen based upon these observations. These included total variation minimization (TV-L1), non-local means filter (NLM), block-matching 3D (BM3D), alpha (adaptive) trimmed mean (ATM), and bilateral filtering. Each were applied to images acquired using a BeamSite camera (DoseOptics) imaged signal from 6x photons from a TrueBeam linac delivering dose at 600 MU min -1 incident on an anthropomorphic phantom and tissue slab phantom in various configurations and beam angles. The standard denoised images were tested for PSNR, noise power spectrum (NPS) and image sharpness. Results. The average peak-signal-to-noise ratio (PSNR) increase was 17.4% for TV-L1. NLM denoising increased the average PSNR by 19.1%, BM3D processing increased it by12.1% and the bilateral filter increased the average PSNR by 19.0%. Lastly, the ATM filter resulted in the lowest average PSNR increase of 10.9%. Of all of these, the NLM and bilateral filters produced improved edge sharpness with, generally, the lowest NPS curve. Conclusion. For cumulative image Cherenkov data, NLM and the bilateral filter yielded optimal denoising with the TV-L1 algorithm giving comparable results. Single video frame Cherenkov images exhibit much higher noise levels compared to cumulative images. Noise suppression algorithms for these frame rates will likely be a different processing pipeline involving these filters incorporated with machine learning., (Creative Commons Attribution license.)

Image block matching is one of the representative methods for image detection and motion compensation in MPEG. Block matching between two images is a problem of finding symmetry between two images by matching macro blocks that are symmetrical to each other in two given images. The greater the PSNR value, the greater the symmetry of the two images. In the given two images, the two macro blocks with the minimum matching error values are regarded as symmetrical to each other. The classical method of calculating the matching error function for every pixel in the entire search area and choosing the smallest of them guarantees global convergence but requires a lot of computation, especially for large intensities. For this reason, many sparse search methods have been developed to reduce the amount of computation. In this paper, we introduce a gradient descent vector optimization algorithm with guaranteed global convergence to the image block matching problem by utilizing the conceptual symmetry of the vector optimization problem, which is a continuous variable, and the image block matching problem, which is a discrete variable. By blurring the image, we transform the matching cost function closer to being unimodal so that the descent-type algorithm works well. As a result, although the proposed method is simple, it can reduce the amount of computation remarkably and has more robustness for the large displacement of image blocks compared to existing sparse search methods.

Due to the strong correlation between symmetric frames, video signals have a high degree of temporal redundancy. Motion estimation techniques are computationally expensive and time-consuming processes used in symmetric video compression to reduce temporal redundancy. The block-matching technique is, on the other hand, the most popular and efficient of the different motion estimation and compensation techniques. Motion compensation based on the block-matching technique generally uses the minimization of either the mean square error (MSE) or mean absolute difference (MAD) in order to find the appropriate motion vector. This paper proposes to remove the highly temporally redundant information contained in each block of the video signal using the removing temporal redundancy (RTR) technique in order to improve the data rate and efficiency of the video signal. A comparison between the PSNR values of this technique and those of the JPEG video compression standard is made. As a result of its moderate memory and computation requirements, the algorithm was found to be suitable for mobile networks and embedded devices. Based on a detailed set of testing scenarios and the obtained results, it is evident that the RTR compression technique allowed a compression ratio of 22.71 and 95% loss in bit rate reduction while maintaining sufficient intact signal quality with minimized information loss.

Blind image forgery prediction in the field of image forgery is difficult. Hence, it is the major attention for the investigators recently. This work introduces an innovative methodology for blind image forgery detection. The steps are described as: initially, input images are pre-processed using hybrid homomorphic filtering to enhance the images. After pre-processing images, features such as Hybrid Speeded up Robust features and scale-invariant feature transform (hybrid SURF-SIFT) and hybrid wavelet features are extracted. According to the extracted features hybrid deep belief neural network (HDBN) framework performs the block matching to examine the forgery region in images. Here, modified atom search optimization is utilized for weights optimization in HDBN framework and improving the performance of matching process. The HDBN framework detects non matching regions, and finally, the forgery region is accurately localized with the presented Adaptive fuzzy clustering based improved sun flower optimization (AFCSO) approach. The implementation platform used in work is PYTHON. The presented technique is tested with three datasets: CG-1050, SMIFD, and Coverage. Moreover, the experimental results of the presented approach is examined with the existing techniques in regard to accuracy, precision, recall, F-measure, True positive rate, False positive rate, True negative rate, and False-negative rate. The performance measures of forgery detection with CG-1050 dataset is F-measure (98.98%), Accuracy (99%), recall (98%), precision (99.9%), True positive rate (99.5%), False positive rate (0.4%), True negative rate (99.7%), False negative rate (2%). The performance measures of forgery detection with SMIFD dataset is F-measure (99.15%), True positive rate (99.57%), Accuracy (98.67%), precision (99.57%), false-positive rate (1.56%), True negative rate (98.43%), and false-negative rate (1.25%). The performance measures of forgery detection with COVERAGE dataset is F-measure (98%), True positive rate (98%), False positive rate (2%), Accuracy (98.3%), precision (97.1%), True negative rate (98%), False negative rate (2%). This proved that the presented approach outperforms the compared existing approaches. [ABSTRACT FROM AUTHOR]

This work proposes a novel scheme for speckle suppression on medical images acquired by ultrasound sensors. The proposed method is based on the block matching procedure by using mutual information as a similarity measure in grouping patches in a clustered area, originating a new despeckling method that integrates the statistical properties of an image and its texture for creating 3D groups in the BM3D scheme. For this purpose, the segmentation of ultrasound images is carried out considering superpixels and a variation of the local binary patterns algorithm to improve the performance of the block matching procedure. The 3D groups are modeled in terms of grouped tensors and despekled with singular value decomposition. Moreover, a variant of the bilateral filter is used as a post-processing step to recover and enhance edges' quality. Experimental results have demonstrated that the designed framework guarantees a good despeckling performance in ultrasound images according to the objective quality criteria commonly used in literature and via visual perception. [ABSTRACT FROM AUTHOR]

Constructing effective priors is critical to solving ill-posed inverse problems in image processing and computational imaging. Recent works focused on exploiting non-local similarity by grouping similar patches for image modeling, and demonstrated state-of-the-art results in many image restoration applications. However, compared to classic methods based on filtering or sparsity, non-local algorithms are more time-consuming, mainly due to the highly inefficient block matching step, i.e., distance between every pair of overlapping patches needs to be computed. In this work, we propose a novel Self-Convolution operator to exploit image non-local properties in a unified framework. We prove that the proposed Self-Convolution based formulation can generalize the commonly-used non-local modeling methods, as well as produce results equivalent to standard methods, but with much cheaper computation. Furthermore, by applying Self-Convolution, we propose an effective multi-modality image restoration scheme, which is much more efficient than conventional block matching for non-local modeling. Experimental results demonstrate that (1) Self-Convolution with fast Fourier transform implementation can significantly speed up most of the popular non-local image restoration algorithms, with two-fold to nine-fold faster block matching, and (2) the proposed online multi-modality image restoration scheme achieves superior denoising results than competing methods in both efficiency and effectiveness on RGB-NIR images. The code for this work is publicly available at https://github.com/GuoLanqing/Self-Convolution. [ABSTRACT FROM AUTHOR]

Copyright of Geography & Geographic Information Science is the property of Geography & Geo-Information Science Editorial Office 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.)

Abstract Background In majority of studies on speckle tracking echocardiography (STE) the strain estimates are averaged over large areas of the left ventricle. This may impair the diagnostic capability of the STE in the case of e.g. local changes of the cardiac contractility. This work attempts to evaluate, how far one can reduce the averaging area, without sacrificing the estimation accuracy that could be important from the clinical point of view. Methods Synthetic radio frequency (RF) data of a spheroidal left ventricular (LV) model were generated using FIELD II package and meshes obtained from finite element method (FEM) simulation. The apical two chamber (A2C) view and the mid parasternal short axis view (pSAXM) were simulated. The sector encompassed the entire cross-section (full view) of the LV model or its part (partial view). The wall segments obtained according to the American Heart Association (AHA17) were divided into subsegments of area decreasing down to 3 mm2. Longitudinal, circumferential and radial strain estimates, obtained using a hierarchical block-matching method, were averaged over these subsegments. Estimation accuracy was assessed using several error measures, making most use of the prediction of the maximal relative error of the strain estimate obtained using the FEM derived reference. Three limits of this predicted maximal error were studied, namely 16.7%, 33% and 66%. The smallest averaging area resulting in the strain estimation error below one of these limits was considered the smallest allowable averaging area (SAAA) of the strain estimation. Results In all AHA17 segments, using the A2C projection, the SAAA ensuring maximal longitudinal strain estimates error below 33% was below 3 mm2, except for the segment no 17 where it was above 278 mm2. The SAAA ensuring maximal circumferential strain estimates error below 33% depended on the AHA17 segment position within the imaging sector and view type and ranged from below 3–287 mm2. The SAAA ensuring maximal radial strain estimates error below 33% obtained in the pSAXM projection was not less than 287 mm2. The SAAA values obtained using other maximal error limits differ from SAAA values observed for the 33% error limit only in limited number of cases. SAAA decreased when using maximal error limit equal to 66% in these cases. The use of the partial view (narrow sector) resulted in a decrease of the SAAA. Conclusions The SAAA varies strongly between strain components. In a vast part of the LV model wall in the A2C view the longitudinal strain could be estimated using SAAA below 3 mm2, which is smaller than the averaging area currently used in clinic, thus with a higher resolution. The SAAA of the circumferential strain estimation strongly depends on the position of the region of interest and the parameters of the acquisition. The SAAA of the radial strain estimation takes the highest values. The use of a narrow sector could increase diagnostic capabilities of 2D STE.

By detecting and identifying the vanishing point position in the image, it is able to assist mobile robots in mines roadways for autonomous navigation. The existing vanishing point detection methods have large errors in the mine roadway with poor lighting conditions and insufficient structured information. In order to solve the above problems, a vanishing point detection method in complex environment of mine roadways is proposed. Firstly, the image is pre-processed by reducing, filtering, graying, etc. This method can reduce the calculation amount significantly and the straight line characteristics can be better preserved. Then, the straight line detection algorithm is used to detect the straight line of the image. The straight line length threshold and the average gradient constraint are introduced to eliminate the interference line with small length and the interference line generated by shadows in the image respectively. Moreover, the block matching algorithm is used to generate the block motion trajectory straight line of the image. Finally, the straight lines after removing the interference and the block motion trajectory straight lines are converted into sample points in the parameter space. The outlier factor value of each sample point is calculated by the local anomaly factor algorithm, and the outlier factor value of the sample point and the length of the corresponding straight line are used as the criteria to measure the importance of the sample points. On this basis, the weight function of the weighted regression algorithm is designed to obtain the best estimate of the vanishing point. The experimental results on the mine roadway data set and public data set show that compared with the edge-based vanishing point detection method and the deep learning-based vanishing point detection method, the method in this paper has stronger robustness to light changes. It has higher accuracy in complex environment with poor lighting conditions and lack of straight line information, and has better real-time performance than the vanishing point detection method based on deep learning. This method can better meet the needs of mine roadway robot navigation.

Motion Estimation (ME) is a very important operation in video coding. In order to reduce complexity of computations involved in ME and to increase quality of this process, many Block Matching Motion Estimation (BMME) Algorithms are proposed. The aim of this paper is to compare between these algorithms and find the best one. Seven BMME algorithms are used in this paper. The performance of each algorithm is evaluated for different types of motion to determine the best one of these algorithms. The evaluation is based on search points, and Peak Signal to Noise Ratio (PSNR). The simulation shows that Hexagonal Search is faster than all other Block Matching (BM) algorithms used in this paper regardless the type of video because it requires less number of search points to evaluate motion vectors for the video sequence. It requires 11.2424 average search point (SP) for small motions and 13.9708 for fast motions. It also gives a good quality that is close enough to the quality given by Full Search.

Purpose: Spectral computed tomography (CT) based on the photon‐counting detection system has the capability to produce energy‐discriminative attenuation maps of objects with a single scan. However, the insufficiency of photons collected into the narrow energy bins results in high quantum noise levels causing low image quality. This work aims to improve spectral CT image quality by developing a novel regularization based on framelet tensor prior. Methods: First, similar patches are extracted from highly correlated interchannel images in spectral and spatial domains, and stacked to form a third‐order tensor after vectorization along the energy channels. Second, the framelet tensor nuclear norm (FTNN) is introduced and applied to construct the regularization to exploit the sparsity embedded in nonlocal similarity of spectral images, and thus the reconstruction problem is modeled as a constrained optimization. Third, an iterative algorithm is proposed by utilizing the alternating direction method of multipliers framework in which efficient solvers are developed for each subproblem. Results: Both numerical simulations and real data verifications were performed to evaluate and validate the proposed FTNN‐based method. Compared to the analytic, TV‐based, and the state‐of‐the‐art tensor‐based methods, the proposed method achieves higher numerical accuracy on both reconstructed CT images and decomposed material maps in the mouse data indicating the capability in noise suppression and detail preservation of the proposed method. Conclusions: A framelet tensor sparsity–based iterative algorithm is proposed for spectral reconstruction. The qualitative and quantitative comparisons show a promising improvement of image quality, indicating its promising potential in spectral CT imaging. [ABSTRACT FROM AUTHOR]

The ultrasonic technique is an indispensable imaging modality for diagnosis of breast cancer in young women due to its ability in efficiently capturing the tissue properties, and decreasing nega-tive recognition rate thereby avoiding non-essential biopsies. Despite the advantages, ultrasound images are affected by speckle noise, generating fine-false structures that decrease the contrast of the images and diminish the actual boundaries of tissues on ultrasound image. Moreover, speckle noise negatively impacts the subsequent stages in image processing pipeline, such as edge detec-tion, segmentation, feature extraction, and classification. Previous studies have formulated vari-ous speckle reduction methods in ultrasound images; however, these methods suffer from being unable to retain finer edge details and require more processing time. In this study, we propose a breast ultrasound de-speckling method based on rotational invariant block matching non-local means (RIBM-NLM) filtering. The effectiveness of our method has been demonstrated by com-paring our results with three established de-speckling techniques, the switching bilateral filter (SBF), the non-local means filter (NLMF), and the optimized non-local means filter (ONLMF) on 250 images from public dataset and 6 images from private dataset. Evaluation metrics, including Self-Similarity Index Measure (SSIM), Peak Signal to Noise Ratio (PSNR), and Mean Square Error (MSE) were utilized to measure performance. With the proposed method, we were able to record average SSIM of 0.8915, PSNR of 65.97, MSE of 0.014, RMSE of 0.119, and computational speed of 82 seconds at noise variance of 20dB using the public dataset, all with p-value of less than 0.001 compared against NLMF, ONLMF, and SBF. Similarly, the proposed method achieved av-erage SSIM of 0.83, PSNR of 66.26, MSE of 0.015, RMSE of 0.124, and computational speed of 83 seconds at noise variance of 20dB using the private dataset, all with p-value of less than 0.001 compared against NLMF, ONLMF, and SBF. [ABSTRACT FROM AUTHOR]

BM3D-based denoising has been showing high performance in restoring images damaged by additive white noise and there has been intense research on this method and its variants. In this paper, we make three improvements on BM3D (Block-matching and 3-dimensional filtering). Block matching performs poor and affect denoising performance, especially if noise intensity is high. In the paper, we first proposed a new block similarity metric that accounts for characteristic of noise contained in the observed images in order to guarantee accuracy of block matching even in presence of high intensity noise. Second, block size is a crucial hyperparameter for BM3D. The optimal block size varies with the characteristic of images. However, it is difficult to determine such an optimal block size. We proposed a method to mitigate this difficulty in determining optimal block sizes by combining BM3D and multi-scaled images. Finally, in Aggregation of BM3D, the same weight is assigned to every block in three-dimensional structures. In fact, however, the degree with which noise is removed in each block is different. We presented a method of assigning different weights to blocks according to their respective denoising degrees. Experimental results show that the proposed method is competitive with BM3D and even many of state-of-the-art methods. Actually, it brings about 0.1 ~ 0.6 dB pickup in the PSNR (Peak Signal to Noise Ratio) value. Also, we recommend that it may get better results by applying ideas proposed in this paper individually to state-of-the-art methods. [ABSTRACT FROM AUTHOR]

Aiming at insufficient detailed description problem caused by the loss of edges during a single low-resolution (LR) image's reconstruction process, a novel algorithm for super resolution image reconstruction is proposed in this paper, which is based on fusion of internal structural self-similarity dictionary and external convolution neural network parameters learning model. Firstly, for solving training samples too scattered problem, besides external database, an internal database is constructed to learn a dictionary of the single image's structural self-similarity by multi-scale decomposition approach. Secondly, nonlocal regularization constraint is calculated on the priori knowledge, which is obtained from the internal database of the single LR image. Thirdly, similar block pairs of high and low-resolution samples in the external database are input into a convolution neural network for learning the parameters of reconstructing model. After all, combined parameters learned and the internal dictionary, the single LR image is reconstructed, and by iterative back-projection algorithm its result is improved. Experimental results show that, compared with state-of-the-art algorithms, such as Bicubic, K-SVD algorithm and SRCNN algorithm, our method is more effective and efficient. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Computer Engineering & Applications is the property of Beijing Journal of Computer Engineering & Applications Journal Co Ltd. 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.)

Image block matching is one of active research fields in image processing, which has been widely used in security monitoring and motion estimation. Due to great disparities in images of the same scene under various illumination, block matching has been a challenging task. To this end, we propose a hierarchical block matching method which is adaptive to the computational complexity and suited to complicated illumination environments. The approach is divided into two parts. First, in order to reduce searching time, the whole algorithm is established in the framework of pyramid structures. Second, the correlation coefficient and structural functions are adopted to evaluate the similarity between two images so as to get better matching results. Simulation results show that, compared with the classical three-step, four-step searching algorithms and OHBM algorithm, this algorithm can reduce the time complexity efficiently. Moreover, since the algorithm makes use of the structure details of images, it is robust to complicated scenes. [ABSTRACT FROM AUTHOR]

Motion estimation plays a very crucial role in many video applications. Hence, an accurate and fast algorithm is required in real-time applications. Although exhaustive block matching (EBM) algorithm is the best algorithm used for motion estimation in terms of performance, it is computationally very expensive. Several algorithms have been developed in the literature to reduce the computational time required by the EBM algorithm; however, their performance is not comparable to that of EBM. In this paper, we propose an extremely fast technique for motion estimation based on the edge map of only a fraction of the frame. Extensive experiments are carried out, and the results show that the proposed technique provides a performance comparable to that of the EBM algorithm, with a very low computational complexity. [ABSTRACT FROM AUTHOR]