Block Matching Algorithm Research Articles

DeepOCT: An explainable deep learning architecture to analyze macular edema on OCT images

Macular edema (ME) is one of the most common retinal diseases that occur as a result of the detachment of the retinal layers on the macula. This study provides computer-aided identification of ME for even small pathologies on OCT using the advantages of Deep Learning. The study aims to identify ME on OCT images using a lightweight explainable Convolutional neural networks (CNN) architecture by composing significant feature activation maps and reducing the trainable parameters. A CNN is an effective Deep Learning algorithm, which consists of feature learning and classification stages. The proposed model, DeepOCT, focuses on reaching high classification performances as well as popular pre-trained architectures using less feature learning and shallow dense layers in addition to visualizing the most responsible regions and pathology on feature activation maps. The DeepOCT encapsulates the block-matching and 3D filtering (BM3D) algorithm, flattening the retinal layers to avoid the effects arising from different macula positions, and excluding non-retinal layers by cropping. DeepOCT identified OCT with ME with the rates of 99.20%, 100%, and 98.40% for accuracy, sensitivity, and specificity, respectively. The DeepOCT provides a standardized analysis, a lightweight architecture by reducing the number of trainable parameters, and high classification performances for both large- and small-scale datasets. It can analyze medical images at different levels with simple feature learning, whereas the literature uses complicated pre-trained feature learning architectures.

An Optimal Method for Extracting the Center of Light Stripes

A method for extracting the center of the light stripe to effectively reduce the environmental noise is proposed in this paper. The block matching algorithm is adapted to use the global information in the structured light image to group image blocks with similar light stripe structures. The center coordinates of the light stripe in each group of image blocks are extracted by the gray gravity method, and its average value is used as the final center of light stripes in the similar image block, which reduces the influence of random noise on the accuracy of the extraction algorithm.

Accelerated Fast BOTDA Assisted by Compressed Sensing and Image Denoising

We propose and experimentally demonstrate a scheme for accelerated fast BOTDA. The effect of signal-to-noise ratio (SNR) on recovery performance of compressed sensing is simulated and analyzed, it is found that a reduction in SNR requires much larger frequency data to recover the original Brillouin gain spectrum (BGS). To enable a high recovery probability, Block-Matching and 3D filtering (BM3D) algorithm is employed to enhance the SNR of Brillouin time trace and reduce the number of averages. Combining with a principal component analysis (PCA) based compressed sensing technique, the Brillouin gain spectrum (BGS) can be successfully reconstructed from only 37.5% frequency data. In the experiment, 75 randomly selected frequency data is acquired to reconstruct the BGS. Distributed strain sensing is achieved over 15 km single-mode fiber with 3 m spatial resolution and 0.52 MHz Brillouin frequency shift (BFS) uncertainty. Due to the accelerated process, the measurement time with 40 averages is less than 0.5 s.

Bayesian Regularized Strain Imaging for Assessment of Murine Cardiac Function In vivo.

A cardiac strain imaging framework with adaptive Bayesian regularization (ABR) is proposed for in vivo assessment of murine cardiac function. The framework uses ultrasound (US) radio-frequency data collected with a high frequency (fc = 30MHz) imaging system and a multi-level block matching algorithm with ABR to derive inter-frame cardiac displacements. Lagrangian cardiac strain (radial, er and longitudinal, el) tensors were derived by segmenting the myocardial wall starting at the ECG R-wave and accumulating interframe deformations over a cardiac cycle. In vivo feasibility was investigated through a longitudinal study with two mice (one ischemia-perfusion (IR) injury and one sham) imaged at five sessions (pre-surgery (BL) and 1,2,7 and 14 days post-surgery). End-systole (ES) strain images and segmental strain curves were derived for quantitative evaluation. Both mice showed periodic variation of er and el strain at BL with segmental synchroneity. Infarcted regions of IR mouse at Day 14 were associated with reduced or sign reversed ES er and el values while the sham mouse had similar or higher strain than at BL. Infarcted regions identified in vivo were associated with increased collagen content confirmed with Masson's Trichrome stained ex vivo heart sections.Clinical Relevance-Higher quality cardiac strain images derived with RF data and Bayesian regularization can potentially improve the sensitivity and accuracy of non-invasive assessment of cardiovascular disease models.

Correction to: An Efficient VLSI Architecture for Fast Motion Estimation Exploiting Zero Motion Prejudgment Technique and a New Quadrant-Based Search Algorithm in HEVC

In this manuscript, new quadrant-based search algorithm with zero motion prejudgment is proposed for motion estimation (ME) in HEVC (High Efficiency Video Coding) standard. The HEVC standard is used to obtain efficient output with low motion estimation time. The proposed quadrant-based search algorithm is a fast block matching algorithm that obtain better block matching amid the current block and reference block. The zero motion prejudgment (ZMP) method is used to find the block, whether it is motion or static and it is used for decreasing the computational complexity (CC) in the proposed quadrant-based search algorithm. The proposed quadrant-based search algorithm with ZMP technique for motion estimation in HEVC is implemented on the FPGA hardware platform. The entire architecture is executed in Verilog HDL with Virtex-5 technology and integrated with Xilinx ISE Design Suite 14.5. The results are integrated into the CIF (352 × 288 pixels) and HD (1280 × 720 pixels) video input sequence. The evaluation metrics like PSNR, Motion estimation time, sum of absolute difference (SAD) value are analyzed with existing method like hexagon, adaptive root pattern algorithm, and diamond search algorithm. Then the hardware parameters like power consumption and maximum operating frequency are measured. The hardware utilization is reduced and the power consumption of the proposed model is diminished to 0.143 W. The maximal operating frequency of the proposed model is 440.470 MHz. The experimental outcomes demonstrate that the proposed motion evaluation approach in HEVC is more effective than existing algorithms.

Three-dimensional reconstruction of porous media using super-dimension-based adjacent block-matching algorithm.

As porous media play an essential role in a variety of industrial applications, it is essential to understand their physical properties. Nowadays, the super-dimensional (SD) reconstruction algorithm is used to stochastically reconstruct a three-dimensional (3D) structure of porous media from a given two-dimensional image. This algorithm exhibits superiority in accuracy compared with classical algorithms because it learns information from the real 3D structure. However, owing to the short development time of the SD algorithm, it also has some limitations, such as inexact porosity characterization, long run time, blocking artifacts, and suboptimal accuracy that may be improved. To mitigate these limitations, this study presents the design of a special template that contains two parts of data (i.e., adjacent blocks and a central block); the proposed method matches adjacent blocks during reconstruction and assigns the matched central block to the area to be reconstructed. Furthermore, we design two important mechanisms during reconstruction: one for block matching and the other for porosity control. To verify the effectiveness of the proposed method compared with an existing SD method, both methods were tested on silica particle material and three homogeneous sandstones with different porosities; meanwhile, we compared the proposed method with a multipoint statistics method and a simulated annealing method. The reconstructed results were then compared with the target both visually and quantitatively. The experimental results indicate that the proposed method can overcome the aforementioned limitations and further improve the accuracy of existing methods. This method achieved 4-6 speedup factor compared with the traditional SD method.

An efficient field-programmable gate array-based hardware oriented block motion estimation algorithm based on diamond adaptive rood pattern search algorithm for multi-standard video codec

Motion estimation and motion compensation techniques are effectively used for removing temporal redundancy between adjacent frames during video compression, but video quality will be degraded with the traditional pixel-based matching algorithms. To overcome this issue, diamond adaptive rood pattern search algorithm-based block-matching motion estimation algorithms are proposed in this manuscript for multi-stranded codec hardware design, which provide a high compression rate with less computational complexity. In this work, a diamond adaptive rood pattern search algorithm is used, which is a combination of a diamond-search algorithm and adaptive rood pattern search. In addition, the proposed algorithm applies multiple search strategies and terminates unnecessary searching while reserving high data regularity. The proposed diamond adaptive rood pattern search algorithm-based block-matching motion estimation algorithm for multi-stranded codec hardware design provides good performance in terms of complex calculations reduction and reaches a high speed in comparison to traditional search algorithms. The proposed architecture is implemented in Xilinx ISE design 14.5 suite and the implemented results are compared to the existing motion estimation techniques on field-programmable gate array devices. Simulation results shows that the diamond adaptive rood pattern search algorithm for multi-stranded codec hardware design provides a very good improvement in terms of computations overhead reduction, with 96.4%, 97.3% latency and 98.7% throughput and achieves a high compression rate without video quality degradation.

A Framework for Identification of Healthy Potted Seedlings in Automatic Transplanting System Using Computer Vision.

Automatic transplanting of seedlings is of great significance to vegetable cultivation factories. Accurate and efficient identification of healthy seedlings is the fundamental process of automatic transplanting. This study proposed a computer vision-based identification framework of healthy seedlings. Vegetable seedlings were planted in trays in the form of potted seedlings. Two-color index operators were proposed for image preprocessing of potted seedlings. An optimal thresholding method based on the genetic algorithm and the three-dimensional block-matching algorithm (BM3D) was developed to denoise and segment the image of potted seedlings. The leaf area of the potted seedling was measured by machine vision technology to detect the growing status and position information of the potted seedling. Therefore, a smart identification framework of healthy vegetable seedlings (SIHVS) was constructed to identify healthy potted seedlings. By comparing the identification accuracy of 273 potted seedlings images, the identification accuracy of the proposed method is 94.33%, which is higher than 89.37% obtained by the comparison method.

An efficient field programmable gate array based hardware architecture for efficient motion estimation with parallel implemented genetic algorithm

SummaryThe main idea of this article is to propose a hardware design of block matching (BM) algorithm for an efficient motion estimation (ME) strategy in a field programmable gate array platform based on a parallel implemented genetic algorithm (GA). Easiness and the effectiveness of the BM algorithm while implementing have a major drawback of low quality and computationally cost expensive during the process of ME. Therefore, here in this article, we suggest GA based BM for a quick and cost‐effective computation of motion vectors, without negotiating the quality factor. The ME carried out for various video sequences is implemented by using Xilinx ISE Design Suite 14.1. Delay, time, area, power, PSNR, MSE, SNR, SSIM, and NRMSE are the metrics used for analyzing the performance, and the simulation outcome shows that this parallel implemented BM architecture design shows an exotic improvement in time, quality and in utilization of power on estimating the motion than that of the conventional designs.

Multi-Scale Superpixels Dimension Reduction Hyperspectral Image Classification Algorithm Based on Low Rank Sparse Representation Joint Hierarchical Recursive Filtering.

The original Hyperspectral image (HSI) has different degrees of Hughes phenomenon and mixed noise, leading to the decline of classification accuracy. To make full use of the spatial-spectral joint information of HSI and improve the classification accuracy, a novel dual feature extraction framework joint transform domain-spatial domain filtering based on multi-scale-superpixel-dimensionality reduction (LRS-HRFMSuperPCA) is proposed. Our framework uses the low-rank structure and sparse representation of HSI to repair the unobserved part of the original HSI caused by noise and then denoises it through a block-matching 3D algorithm. Next, the dimension of the reconstructed HSI is reduced by principal component analysis (PCA), and the dimensions of the reduced images are segmented by multi-scale entropy rate superpixels. All the principal component images with superpixels are projected into the reconstructed HSI in parallel. Secondly, PCA is once again used to reduce the dimension of all HSIs with superpixels in scale with hyperpixels. Moreover, hierarchical domain transform recursive filtering is utilized to obtain the feature images; ultimately, the decision fusion strategy based on a support vector machine (SVM) is used for classification. According to the Overall Accuracy (OA), Average Accuracy (AA) and Kappa coefficient on the three datasets (Indian Pines, University of Pavia and Salinas), the experimental results have shown that our proposed method outperforms other state-of-the-art methods. The conclusion is that LRS-HRFMSuperPCA can denoise and reconstruct the original HSI and then extract the space-spectrum joint information fully.

Robust block-matching algorithm for motion estimation using an anti-interference similarity criterion and the bilateral optimization scheme.

As an essential component in applications such as video coding, autonomous navigation, and surveillance cameras, efficient and robust motion estimation is always required. This paper proposes a robust block-matching algorithm consisting of a rough matching step and a fine matching step for motion estimation. In the coarse matching step, an improved adaptive rood pattern search strategy combined with an anti-interference similarity criterion is developed to improve the computational efficiency and robustness. In the fine matching step, after performing a subpixel estimation procedure, a bilateral verification scheme is demonstrated to decrease the motion estimation errors caused by environmental disturbances. Experiments are carried out over popular video and image sequences, and several measurement indexes are used to quantify the performance of the proposed method and other motion estimation methods. Comparative analysis and quantitative evaluation demonstrate that the proposed method exhibits strong robustness and can achieve a good balance between computational efficiency and complexity.

Assessing the short-term effects of radiotherapy on the shear modulus of the common carotid artery as a new biomarker of radiation-induced atherosclerosis.

PurposeThe purpose of this study was to investigate the incidence of short-term atherosclerosis in the common carotid arteries following radiotherapy.MethodsThe mean radiation dose to the arteries was 49.30±15.83 Gy. A computational ultrasound method was introduced to investigate the elastic modulus. Ultrasonography was performed 2-3 cm inferior to the bifurcation region before and after radiotherapy, and sequential images were extracted from a video of each artery. Instantaneous movement of the arterial wall in the radial and longitudinal directions was extracted by implementing the maximum gradient and block matching algorithms, respectively.ResultsThere was a significant change in systolic blood pressure after radiotherapy (P=0.008). Irradiated arteries had significantly smaller systolic and end-diastolic diameters than non-irradiated arteries (P<0.001). The shear modulus was significantly different between irradiated and non-irradiated arteries (3.10±2.03 kPa vs. 1.38±0.98 kPa, P<0.001). The shear and Young moduli of radiation-induced arteries were 2.25±1.50 and 1.57±0.59 times higher than those of the pre-irradiation arteries.ConclusionThe arterial shear modulus can be considered as a new biomarker of radiation-induced atherosclerosis in the common carotid artery.

State-Of-The-Art In Video Processing: Compression, Optimization And Retrieval

Video compression plays a vital role in the modern social media networking with plethora of multimedia applications. It empowers transmission medium to competently transfer videos and enable resources to store the video efficiently. Nowadays high-resolution video data are transferred through the communication channel having high bit rate in order to send multiple compressed videos. There are many advances in transmission ability, efficient storage ways of these compressed video where compression is the primary task involved in multimedia services. This paper summarizes the compression standards, describes the main concepts involved in video coding. Video compression performs conversion of large raw bits of video sequence into a small compact one, achieving high compression ratio with good video perceptual quality. Removing redundant information is the main task in the video sequence compression. A survey on various block matching algorithms, quantization and entropy coding are focused. It is found that many of the methods having computational complexities needs improvement with optimization.

A very fast edge map-based algorithm for accurate motion estimation

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.

Motion Estimation for Gray Level Videos Using Different Block Matching Algorithms

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

Frequency division denoising algorithm based on VIF adaptive 2D-VMD ultrasound image.

Ultrasound imaging has developed into an indispensable imaging technology in medical diagnosis and treatment applications due to its unique advantages, such as safety, affordability, and convenience. With the development of data information acquisition technology, ultrasound imaging is increasingly susceptible to speckle noise, which leads to defects, such as low resolution, poor contrast, spots, and shadows, which affect the accuracy of physician analysis and diagnosis. To solve this problem, we proposed a frequency division denoising algorithm combining transform domain and spatial domain. First, the ultrasound image was decomposed into a series of sub-modal images using 2D variational mode decomposition (2D-VMD), and adaptively determined 2D-VMD parameter K value based on visual information fidelity (VIF) criterion. Then, an anisotropic diffusion filter was used to denoise low-frequency sub-modal images, and a 3D block matching algorithm (BM3D) was used to reduce noise for high-frequency images with high noise. Finally, each sub-modal image was reconstructed after processing to obtain the denoised ultrasound image. In the comparative experiments of synthetic, simulation, and real images, the performance of this method was quantitatively evaluated. Various results show that the ability of this algorithm in denoising and maintaining structural details is significantly better than that of other algorithms.

One-dimensional Block Matching Algorithm in Video Image Motion Estimation

<p indent=0mm>Motion estimation is a basic problem in the fields of video image compression and video image restoration. The traditional block matching methods have good search quality, but the search speed is not fast enough. Aiming at the shortcomings of the search speed in the traditional block matching methods, we proposed a fast one-dimensional block matching motion estimation algorithm. Firstly, the motion vector is orthogonally decomposed, and the two-dimensional matching block is reduced by a special weight coefficient matrix to obtain two sets of one-dimensional feature matrices. Then the one-dimensional three step search method is selected as the search strategy. The sum of absolute differences criterion is used as the matching criterion. The two sets of one-dimensional feature matrices are used to search for the two components of the matching motion vector. Finally the two components are formed into a complete motion vector. The results of multiple sets of comparative experiments show that the search speed of motion estimation is significantly improved while the algorithm is guaranteed to quantitatively evaluate the PSNR. The higher the video definition and the larger the pixel size of the matching block, the better the algorithm can improve the search speed of motion estimation.

A Novel Kite Cross Hexagonal Search Algorithm for Fast Block Motion Estimation

The performance quality and searching speed of Block Matching (BM) algorithm are affected by shapes and sizes of the search patterns used in the algorithm. In this paper, Kite Cross Hexagonal Search (KCHS) is proposed. This algorithm uses different search patterns (kite, cross, and hexagonal) to search for the best Motion Vector (MV). In first step, KCHS uses cross search pattern. In second step, it uses one of kite search patterns (up, down, left, or right depending on the first step). In subsequent steps, it uses large/small Hexagonal Search (HS) patterns. This new algorithm is compared with several known fast block matching algorithms. Comparisons are based on search points and Peak Signal to Noise Ratio (PSNR). According to results obtained in this paper, KCHS needs less search time than others algorithms and gives very acceptable performance quality.

Micro-expression recognition based on motion detection method

Micro-expressions are emotional representations that occur spontaneously and cannot be controlled by humans. The micro-expression movements are temporary with fast duration and have subtle movements with little intensity. This is difficult to detect with the human eye. Previous studies have shown that micro-expression movements occur in several areas of the face. This study aims to determine the subtle movements in several areas of the face using the motion detection method. We compared the performance of two motion detection methods: the optical flow method and the Block Matching Algorithm (BMA) method. The optical flow method uses the Kanade-Lucas Tomasi (KLT) method and the BMA method uses the Phase Only Correlation (POC) algorithm. Observations were carried out based on region, where the face area was divided into several observation areas: eyebrows, eyes and mouth. Both methods perform motion detection between frames. The KLT method tracks the movement of the observation points on the frame movement. Meanwhile, the POC method matches the blocks between frames. If the two blocks are the same, no motion vector is generated. However, if the two blocks are different, it is assumed that there is a translational motion and a motion vector is generated. Experiments were conducted using a dataset from CASME II with emotional classes of disgust, happiness, surprise, and sadness. The classification accuracy of the POC method is 94% higher than the KLT method of 84.8% which uses the SVM classification.

3-D Ultrasound Elastography Reconstruction Using Acoustically Transparent Pressure Sensor on Robotic Arm

Biopsy of breast lesions only visible on MRI may benefit from patient-specific finite-element modeling of the breast. This requires acquisition of the heterogeneous elasticity distribution. An acoustically transparent pad has been developed which enables strain elastography imaging using ultrasound without need for additional force or pressure sensors. The elasticity of the pad is known a priori while the change in thickness is detectable on ultrasound by edge detection. Based on the calculated pressure distribution across the pad, the pressure in inner tissue can be approximated. The strain is measured using a cross-correlation block matching algorithm and from these measurements the heterogeneous elasticity distribution can be estimated. The ultrasound probe with pad is moved over the breast surface by a robot arm. At sixteen sites on the surface of one phantom the probe with pad performed an inward push movement perpendicular to the breast surface. The ultrasound scans were post-processed to reconstruct a heterogeneous elasticity map in the local imaging plane. While it is challenging to accurately estimate the stress and strain in deeper tissue, certain features such as the skin can be effectively distinguished from the underlying soft tissue which demonstrates the potential of the system.