Block Matching Research Articles

Respiratory Motion Correction for Compressively Sampled Free Breathing Cardiac MRI Using Smooth l1-Norm Approximation.

Transformed domain sparsity of Magnetic Resonance Imaging (MRI) has recently been used to reduce the acquisition time in conjunction with compressed sensing (CS) theory. Respiratory motion during MR scan results in strong blurring and ghosting artifacts in recovered MR images. To improve the quality of the recovered images, motion needs to be estimated and corrected. In this article, a two-step approach is proposed for the recovery of cardiac MR images in the presence of free breathing motion. In the first step, compressively sampled MR images are recovered by solving an optimization problem using gradient descent algorithm. The L1-norm based regularizer, used in optimization problem, is approximated by a hyperbolic tangent function. In the second step, a block matching algorithm, known as Adaptive Rood Pattern Search (ARPS), is exploited to estimate and correct respiratory motion among the recovered images. The framework is tested for free breathing simulated and in vivo 2D cardiac cine MRI data. Simulation results show improved structural similarity index (SSIM), peak signal-to-noise ratio (PSNR), and mean square error (MSE) with different acceleration factors for the proposed method. Experimental results also provide a comparison between k-t FOCUSS with MEMC and the proposed method.

A Novel Search Area Data Reuse Method for Memory Access Optimization of Fast Search Motion Estimation

Motion estimation (ME) is a time-consuming algorithm to find a matching block in the search area for video applications, such as video compression. Motion estimation algorithm includes full search algorithm and fast search algorithm. If the width of the motion estimation algorithm search area is twice the size of the block, half of the search area for adjacent blocks overlaps. In view of this, this paper proposes a search area data reuse method for fast search motion estimation. With the proposed data reuse implementation, only the first block needs to read the data of the entire search area from the off-chip memory. The other blocks only need to read half the original search area. In this way, time of access to external memory is reduced, and running time of the algorithm is also decreased. Experimental results of diamond search show that the search area data reuse methods can reduce the running time by 40% to 60% compared with the algorithm of no use data reuse, and it can also reduce the power consumption by 62% to 73%. Compared with other methods in the literature, the proposed method also performs better on running time and power consumption.

Location-Aware Influence Blocking Maximization in Social Networks

In real social networks, it is often the case that opposite opinions, ideas, products, or innovations are propagating simultaneously. Although the competitive influence problem has been extensively studied, existing works neglect the fact that the location information can play an important role in influence propagation. In this paper, we study the location-aware influence blocking maximization (LIBM) problem, which aims to find a positive seed set to maximize the blocked negative influence for a given query region. In order to overcome low efficiency of the greedy algorithm, we propose two heuristic algorithms LIBM-H and LIBM-C based on the quadtree index and the maximum influence arborescence structure. Experimental results on real-world datasets show that both LIBM-H and LIBM-C are able to achieve a matching blocking effect to the greedy algorithm and often better than other heuristic algorithms, whereas they are several orders of magnitude faster than the greedy algorithm.

Real-Time Video Stitching Using Camera Path Estimation and Homography Refinement

We propose a novel real-time video stitching method using camera path estimation and homography refinement. The method can stably stitch multiple frames acquired from moving cameras in real time. In the proposed method, one initial between-camera (BC) homography and each camera path (CP) homography are used to estimate the BC homography at every frame. The BC homography is refined by using block matching to adjust the errors of estimated CPs (homography refinement). For fast processing, we extract features using the difference of intensities and use the optical flow to estimate camera motion (CM) homographies, which are multiplied with the previous CMs to calculate CPs (camera path estimations). In experiments, we demonstrated the performance of the CP estimation and homography refinement approach by comparing it with other methods. The experimental results show that the proposed method can stably stitch two image sequences at a rate exceeding 13 fps (frames per second).

Automatic colourization of grayscale images based on tensor decomposition

In this paper a colourizing technique based on some tensor properties is proposed. Toward this goal, it is clarified that tensor decomposition possesses the ability of extracting and gathering overall colour information. The methodology considers a grayscale pixel as a balanced vector in RGB colour space. Any deviation to unbalance the colour coordinates means adding colour information to the initial pixel. For finding the appropriate direction of deviation, the proposed technique uses tensor decomposition to extract colour information from a block divided exemplar colour image called reference. Then apply this direction to the best matched block of the grayscale image based on a similarity criterion while its basic structure is preserved. Finally by retrieving from tensor space into spatial domain the conversion is fulfilled. The similarity criteria for block matching and the plausibility of the system output are the most challenging problems. Both images blocks are considered as 3D tensors and Tucker3 with its unique properties is utilized for transferring the colour information. The novelty, simplicity, accuracy, and the conversion speed are some parameters which are introduced and developed by the proposed algorithm. This approach proves that in comparison with spatial or frequency domain, transforming the colour information into tensor space make it more clear and give us better ability of rendering. The results show that the proposed algorithm is able to present the average structural similarity up to 94%.

Rigid blocks matching method based on contour curves and feature regions

This study proposes a blocks matching method based on contour curves and feature regions that improve the matching precision and speed with which rigid blocks with a specified thickness in point clouds are matched. The method comprises two steps: coarse matching and fine matching. In the coarse matching step, the rigid blocks are first segmented into a series of surfaces and the fracture surfaces are distinguished. Then, the contour curves of the fracture surfaces are extracted using an improved boundary growth method and the rigid blocks are coarsely matched with them. In the fine matching step, feature regions are first extracted from the fracture surfaces. Then, the centroid of each feature region is calculated and the fine matching of rigid blocks with the centroid sets is completed using an improved iterative closest point (ICP) algorithm. The improved ICP algorithm integrates the rotation angle constraint and dynamic iteration coefficient into a probability ICP algorithm, which significantly improves matching precision and speed. Experiments conducted using public blocks and Terracotta Warriors blocks indicate that the proposed method carries out rigid blocks matching more accurately and rapidly than various conventional methods.

Merge Mode for Deformable Block Motion Information Derivation

Motion estimation (ME) plays an important role in most video coding standards since it can improve the coding performance significantly. Block matching ME (BMME) based on translation motion model is employed in both H.264/Advanced Video Coding and High Efficiency Video Coding (HEVC), which assumes that all the pixels in a block undergo the same motion. However, BMME cannot represent the complex motion accurately, such as rotation, zoom, and deformation motion. To resolve the problem, a merge mode for deformable block motion information derivation is proposed, which considers bilinear interpolation model, six-parameter affine model, and four-parameter affine model simultaneously. The sum of absolute transformed differences criterion is used to select the best model for a block. In the proposed merge mode, the motion information of the current block is derived from the motion information of its neighbors. It does not need to perform ME and transmit motion parameters. The proposed merge mode is integrated into HEVC test model (HM) 14.0. The simulation results show that compared with HM14.0, the proposed merge mode can bring 1.3%, 1.7%, and 2.7% Bjontegaard delta (BD) bitrate savings on average for test sequences of HEVC, and 11.0%, 15.2%, and 17.4% BD bitrate savings for deformation sequences in random access main, low-delay B main, and low-delay P main test conditions, respectively, while the increased encoding and decoding complexities are moderate.

Improved BM3D denoising method

Block matching 3D denoising (BM3D) is an excellent single-image denoising method. However, it still needs to be improved for solving practical problems. In this study, the authors attempt to improve the method of BM3D. First, one of the problems of BM3D is that some of its references cannot perform self-adaption when the noise intensity of the images is changed. Therefore, they propose a method using total variation (TV) to calculate the image noise intensity and make the references perform self-adaption. Second, finding similar blocks in the BM3D method is a time-consuming procedure. To solve this problem, they analyse the relationship between the numbers of similar blocks and denoising effect, improve the process of searching for similar blocks, and reduce the running time. Third, through the experiment they find that the denoising effect of BM3D method in the domain of complex texture is unsatisfactory. Thus, they proposed a hybrid denoising method for the complex texture area, using the new TV model and BM3D method together to restore the image. Their experimental results show that the improved BM3D method performs better than the original BM3D method.

Performance Evaluation of Modified Color Image Steganography Using Discrete Wavelet Transform

AbstractSteganography is the foremost influential approach to hide data. Images serve as the most appropriate cover media for steganography. This paper intends to do a performance evaluation of color images and its comparison with the recently proposed approaches, using the modified technique already proposed for grayscale images, by the authors. This approach hides large data in color image using the blocking concept. The blocking process is applied on approximation coefficients of secret image and detail coefficients of red, green and blue components of cover image. The blocks of detail coefficients are replaced with approximation coefficients of secret image using root mean square error method. The key is used to store the position of best matching blocks. It is being predicated that the work will be able to hide large data in a single image. The stego image (ST) has better visual quality based on the peak signal to noise ratio values.

A block matching based approach with multiple simultaneous templates for the real-time 2D ultrasound tracking of liver vessels.

The implementation of motion management techniques in radiation therapy can aid in mitigating uncertainties and reducing margins. For motion management to be effective, it is necessary to track key structures both accurately and at a real-time speed. Therefore, the focus of this work was to develop a 2D algorithm for the real-time tracking of ultrasound features to aid in radiation therapy motion management. The developed algorithm utilized a similarity measure-based block matching algorithm incorporating training methods and multiple simultaneous templates. The algorithm is broken down into three primary components, all of which use normalized cross-correlation (NCC) as a similarity metric. First, a global feature shift to account for gross displacements from the previous frame is determined using large block sizes which encompass the entirety of the feature. Second, the most similar reference frame is chosen from a series of training images that are accumulated during the first K frames of tracking to aid in contour consistency and provide a starting point for the localized template initialization. Finally, localized block matching is performed through the simultaneous use of both a training frame and the previous frame. The localized block matching utilizes a series of templates positioned at the boundary points of the training and previous contours. The weighted final boundary points from both the previous and the training frame are ultimately combined and used to determine an affine transformation from the previous frame to the current frame. A mean tracking error of 0.72 ± 1.25 mm was observed for 85 point-landmarks across 39 ultrasound sequences relative to manual ground truth annotations. The image processing speed per landmark with the GPU implementation was between 41 and 165 frames per second (fps) during the training set accumulation, and between 73 and 234 fps after training set accumulation. Relative to a comparable multithreaded CPU approach using OpenMP, the GPU implementation resulted in speedups between -30% and 355% during training set accumulation, and between -37% and 639% postaccumulation. Initial implementations indicated an accuracy that was comparable to or exceeding those achieved by alternative 2D tracking methods, with a computational speed that is more than sufficient for real-time applications in a radiation therapy environment. While the overall performance reached levels suitable for implementation in radiation therapy, the observed increase in failures for smaller features, as well as the algorithm's inability to be applied to nonconvex features warrants additional investigation to address the shortcomings observed.

Image-guided depth propagation for 2-D-to-3-D video conversion using superpixel matching and adaptive autoregressive model

We propose image-guided depth propagation for two-dimensional (2-D)-to-three-dimensional (3-D) video conversion using superpixel matching and the adaptive autoregressive (AR) model. We adopt key frame-based depth propagation that propagates the depth map in the key frame to nonkey frames. Moreover, we use the adaptive AR model for depth refinement to penalize depth-color inconsistency. First, we perform superpixel matching to estimate motion vectors at the superpixel level instead of block matching based on the fixed block size. Then, we conduct depth compensation based on motion vectors to generate the depth map in the nonkey frame. However, the size of two superpixels is not exactly the same due to the segment-based matching, which causes matching errors in the compensated depth map. Thus, we introduce an adaptive image-guided AR model to minimize matching errors and produce the final depth map by minimizing AR prediction errors. Finally, we employ depth-image-based rendering to generate stereoscopic views from 2-D videos and their depth maps. Experimental results demonstrate that the proposed method successfully performs depth propagation and produces high-quality depth maps for 2-D-to-3-D video conversion.

Seismic noise suppression using weighted nuclear norm minimization method

The weighted nuclear norm minimization method as an extension of nuclear-norm minimization was applied to image denoising originally. It is a kind of low rank matrix approximation method that can estimate the noiseless matrix from its noise version. The effective structures of image have a certain degree of repeatability and the weighted nuclear norm minimization method just utilizes this property to construct an approximate low rank matrix. Taking into account the spatial characteristics of seismic data and the redundancies of valid information, we propose to adopt the weighted nuclear norm minimization method to suppress seismic random noise. In this method the block matching algorithm is helpful for the recovery of seismic events because the texture blocks sharing the same reflection events are the most similar. Even when the signal to noise ratio is −10dB, this novel method still be able to clearly recover signals. Experiments on both synthetic and real seismic data show that the weighted nuclear norm minimization method can not only suppress the random noise but also better preserves the valid information of seismic signal when compared to the common seismic denoising methods such as the Wavelet and Time Frequency Peak Filter.

A novel search method based on artificial bee colony algorithm for block motion estimation

The large amount of bandwidth that is required for the transmission or storage of digital videos is the main incentive for researchers to develop algorithms that aim at compressing video data while keeping their quality as high as possible. Block matching has been extensively utilized in compression algorithms for motion estimation as they reduce the memory requirements of any video file. A novel method by using modified artificial bee colony algorithm (MABCA) is proposed, which is the combination of basic ABCA and several techniques—initialization based on temporal-spatial correlation, duplicate searching avoidance technique, adaptive search criteria for iteration, and early termination of block matching. Experimental results show that the proposed method has achieved significant improvement over the existing popular block-matching methods in terms of estimation accuracy and computational complexity, especially when dealing with sequences with violent motion. This method can also adaptively adjust the average search point number according to the motion intensity of the different sequences in order to get the best search result at the lowest possible cost.

A Feasibility Study of Sea Ice Motion and Deformation Measurements Using Multi-Sensor High-Resolution Optical Satellite Images

Sea ice motion and deformation have generally been measured using low-resolution passive microwave or mid-resolution radar remote sensing datasets of daily (or few days) intervals to monitor long-term trends over a wide polar area. This feasibility study presents an application of high-resolution optical images from operational satellites, which have become more available in polar regions, for sea ice motion and deformation measurements. The sea ice motion, i.e., Lagrangian vector, is measured by using a maximum cross-correlation (MCC) technique and multi-temporal high-resolution images acquired on 14–15 August 2014 from multiple spaceborne sensors on board Korea Multi-Purpose Satellites (KOMPSATs) with short acquisition time intervals. The sea ice motion extracted from the six image pairs of the spatial resolutions were resampled to 4 m and 15 m yields with vector length measurements of 57.7 m root mean square error (RMSE) and −11.4 m bias and 60.7 m RMSE and −13.5 m bias, respectively, compared with buoy location records. The errors from both resolutions indicate more accurate measurements than from conventional sea ice motion datasets from passive microwave and radar data in ice and water mixed surface conditions. In the results of sea ice deformation caused by interaction of individual ice floes, while free drift patterns of ice floes were delineated from the 4 m spatial resolution images, the deformation was less revealing in the 15 m spatial resolution image pairs due to emphasized discretization uncertainty from coarser pixel sizes. The results demonstrate that using multi-temporal high-resolution optical satellite images enabled precise image block matching in the melting season, thus this approach could be used for expanding sea ice motion and deformation dataset, with an advantage of frequent image acquisition capability in multiple areas by means of many operational satellites.

Optically Remote Noncontact Heart Rates Sensing Technique

Heart rate monitoring via optically remote noncontact technique was reported in this research. A green laser (5 mW, 532±10 nm) was projected onto the left carotid artery. The reflected laser light on the screen carried the deviation of the interference patterns. The interference patterns were recorded by the digital camera. The recorded videos of the interference patterns were frame by frame analysed by 2 standard digital image processing (DIP) techniques, block matching (BM) and optical flow (OF) techniques. The region of interest (ROI) pixels within the interference patterns were analysed for periodically changes of the interference patterns due to the heart pumping action. Both results of BM and OF techniques were compared with the reference medical heart rate monitoring device by which a contact measurement using pulse transit technique. The results obtained from BM technique was 74.67 bpm (beats per minute) and OF technique was 75.95 bpm. Those results when compared with the reference value of 75.43±1 bpm, the errors were found to be 1.01% and 0.69%, respectively.

Block-and-octave constraint SIFT with multi-thread processing for VHR satellite image matching

ABSTRACTVery high-resolution satellite image matching is a challenging task due to local distortion, repetitive structures, intensity changes and low efficiency. In this letter, a novel approach based on the block and octave constraint scale-invariant feature transform (SIFT) is proposed for very high-resolution satellite image matching. Details regarding multi-thread processing are provided. First, the corresponding matching block is observed using rational function model (RFM) prediction. The RFM prediction can provide a geometric constraint. In each block matching, keypoints are classified with the feature octave. The keypoints are matched in the group with the same feature octave. The block and octave constraint can narrow the keypoints search scope and be helpful for acquiring additional matches. Since blocks are independent, each corresponding block matching is parallelized to realize multi-thread processing with open multi-processing (OpenMP). Experiments are designed to test the matching performance and runtime. The results indicate the effectiveness and efficiency of the proposed approach.

Dynamic initial search pattern defined on Cartesian product of neighboring motion vectors for fast block-based motion estimation

Block-based motion estimation is widely used in video compression for reducing the temporal data redundancy. However, it is still a main problem to effectively reduce the computational complexity of motion estimation. The median predictor is usually used for initial search center prediction, however it is not always accurate enough, especially for fast motion sequences. In this paper, a novel dynamic initial search pattern algorithm for fast block-based motion estimation is proposed. Based on the observation that the components of the current motion vector are very similar to the corresponding components of its neighboring motion vectors, Cartesian product of neighboring motion vectors is introduced to generate the proposed dynamic initial search pattern (DISP). And then the cross search pattern is employed to search for the best matching block. The number of search points of the proposed DISP is adaptive to the neighboring correlation of the current block. In fact, the proposed DISP can be considered as a generalization of median prediction scheme and it performs better in capturing the best matching block than median prediction. Experiment results show that the proposed DISP method with small cross search pattern can save about 1.71 search points on average compared with adaptive rood pattern search (ARPS) algorithm and can achieve the similar PSNR to full search (FS) algorithm by combining large cross search pattern.

A Pattern-Based Artificial Bee Colony Algorithm for Motion Estimation in Video Compression Techniques

Block matching (BM) motion estimation plays an inevitable role in video coding applications. BM approaches are used for data compression. The compression is achieved by removing the temporal redundancy in the video sequences. In the BM process, each video frame is subdivided into macroblocks. Each macroblock in the current frame is compared with the previous frame. The main objective is to minimize sum absolute difference. In this work, some modifications have been performed on conventional artificial bee colony algorithm to improve the conventional BM systems. An initial pattern is used in the proposed algorithm to reduce the computational cost. The computational cost is represented in terms of search points and convergence time. Experimental results results show the improvement for the proposed approach over other block matching algorithms in terms of the performance measures.

Pattern Based Motion Estimation using Zero Motion Pre-judgement and Quantum behaved Particle Swarm Optimization

Motion estimation is a fundamental and resource hungry operation in most of the video coding applications. The most popular method used in any video coding application is block matching motion estimation (BMME). This conventional fast motion estimation algorithm adopts a monotonic error surface for faster computation. However, these search techniques may trap at local minima resulting in erroneous motion estimation. To overcome this issue, various evolutionary swarm intelligence based algorithms were proposed. In this paper, a pattern based motion estimation using zero motion prejudgment and Quantum behaved Particle Swarm Optimization (QPSO) algorithms is proposed, referred to as the Pattern Based Motion Estimation (PBME) algorithm. The notion of QPSO improves the diversity in the search space, which enhances the search efficiency and helps in reduction of the computational burden. At the same time, QPSO needs fewer parameters to control. Therefore, the proposed algorithm enhances the estimation accuracy. An initial search pattern (Hexagonal Based Search) was used which speeds the convergence rate of the algorithm. From the simulation results, it was found that the proposed method outperformed the existing fast block matching (BMA) algorithms of the search point reduction by 40–75%

Comparative study of algorithms for cloud motion estimation using sky-imaging data

One way to improve the efficiency of concentrated solar power plants is to use short-term forecasts of Direct Normal Irradiance (DNI) to apply advanced control strategies. These short-term forecasts are obtained using sky imagers along with signal and image processing techniques. An important step in DNI forecasting is cloud motion estimation. Block matching and optical flow algorithms are the most widely used techniques for motion estimation. In this paper, we present a comparative study between the various existing approaches used to estimate the velocity field of clouds. The comparative study is made using a database of images captured by a sky-imaging system developed at PROMES-CNRS laboratory. Computational time and estimation accuracy are the two criteria used to evaluate the performance of each algorithm. The results demonstrate the high performance of optical flow algorithms in terms of accuracy.