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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.

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]

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]

This paper introduces a field-programmable gate array (FPGA)-based one-shot depth acquisition system, which is a kind of Kinect-like Depth sensing. The proposed implementation integrates the random binary pattern with an improved matching method, which is able to obtain dense and precise depth maps. Scene images captured by a camera are transferred to FPGA (Xilinx Virtex7 XC7VX690T2FFG1761C) through a CamerLink interface, and the acquired depth results are sent to host computer through a high-speed USB3.0 interface. An elaborate FPGA architecture is designed to realize efficient and accurate calculations. The proposed system outperforms the existing stereo systems, not only on the quality of depth acquisition, but also on the reconstruction speed for which our system achieves 90 fps at a resolution of 1280 × 960. Besides, Kinect-like depth sensing has an inherent advantage in deployments, much easier than other kinds of systems. Our proposed system has extensive applications not only in the field of vision processing, but also in the fields where block matching is needed.

Noise suppression is a crucial step before seismic data analysis. The noise in desert areas has the characteristics of nonstationary, non-Gaussian, and low-frequency, which makes some traditional methods cannot suppress the noise well. The weighted nuclear norm minimization (WNNM), one of the most effective methods to suppress noise in all low-rank matrix approximation methods, assigns different weights to different singular values. The good denoising performance of the WNNM is based on the accuracy of block matching, and the theoretical basis of block matching is the nonlocal self-similarity of clean signals. However, the noise will destroy the nonlocal self-similarity and greatly affect the accuracy of block matching. In this article, to reduce the influence of noise on block matching, we use a bandpass filter to process each noisy block before block-matching and use the similarity between the filtered blocks to represent the similarity between the original noisy blocks, then stack the most similar noisy blocks to construct a matrix for further denoising. Experimental results demonstrate the efficiency of the proposed method for low-frequency noise suppression. [ABSTRACT FROM AUTHOR]

The accuracy of digital engraving image modeling of traditional modeling methods cannot be effectively guaranteed. To this end, an image solid modeling method based on laser 3D scanning is proposed. First, the pixel to be repaired is determined according to the priority value of the damaged area, and the neighborhood of the pixel is subjected to block matching processing. Then, using the structural similarity between the block to be repaired and each matching block, a corresponding redundant dictionary is established; the damaged region is repaired by solving the sparse representation problem. Constructing an information conduction model of the digital scanning laser three-dimensional scanning image, collecting the information of the digital scanning laser three-dimensional scanning image, and analyzing its characteristics; then performing priority determination of unknown pixels of the digital printing; then using the 5-level wavelet decomposition method for laser three-dimensional scanning Image feature decomposition and information fusion to achieve rationality modeling of digital print laser 3D scanning images. The simulation results show that the laser scanning modeling method is fast and accurate, and can control the modeling error to a low level. The method of reconstructing the three-dimensional scanned image of digital engraving laser has better repair performance, higher feature fusion degree, higher peak signal-to-noise ratio, improved image quality, and more in print restoration and digital reconstruction applications [ABSTRACT FROM AUTHOR]

Recent works on adaptive sparse and on low-rank signal modeling have demonstrated their usefulness in various image/video processing applications. Patch-based methods exploit local patch sparsity, whereas other works apply low-rankness of grouped patches to exploit image non-local structures. However, using either approach alone usually limits performance in image reconstruction or recovery applications. In this work, we propose a simultaneous sparsity and low-rank model, dubbed STROLLR, to better represent natural images. In order to fully utilize both the local and non-local image properties, we develop an image restoration framework using a transform learning scheme with joint low-rank regularization. The approach owes some of its computational efficiency and good performance to the use of transform learning for adaptive sparse representation rather than the popular synthesis dictionary learning algorithms, which involve approximation of NP-hard sparse coding and expensive learning steps. We demonstrate the proposed framework in various applications to image denoising, inpainting, and compressed sensing based magnetic resonance imaging. Results show promising performance compared to state-of-the-art competing methods. [ABSTRACT FROM AUTHOR]

Depth sensing devices enabled with an RGB camera, can be used to augment conventional images with depth information on a per-pixel basis. Currently available RGB-D sensors include the Asus Xtion Pro, Microsoft Kinect and Intel RealSense™. However, these sensors have certain limitations. Objects that are shiny, transparent or have an absorbing matte surface, create problems due to reflection. Also, there can be an interference in the IR pattern due to the use of multiple RGB-D cameras and the depth information is correctly interpreted only for short distances between the camera and the object. The proposed system, block matching stereo vision (BMSV) uses an RGB-D camera with rectified/non-rectified block matching and image pyramiding along with dynamic programming for human tracking and capture of accurate depth information from shiny/transparent objects. Here, the IR emitter generates a known IR pattern and the depth information is recovered by comparing the multiple views of the focused object. The depth map of the BMSV RGB-D camera and the resultant disparity map are fused. This fills any void regions that may have emerged due to interference or because of the reflective transparent surfaces and an enhanced dense stereo image is obtained. The proposed method is applied to a 3D realistic head model, a functional magnetic resonance image (fMRI) and the results are presented. Results showed an improvement in speed and accuracy of RGB-D sensors which in turn provided accurate depth information density irrespective of the object surface. [ABSTRACT FROM AUTHOR]

This paper presents an enhanced microscopic vision-based approach supporting fast and high-precision measurement on planar motions of a multi-degree of freedom (DOF) compliant stage with sub-micrometer motion accuracy. The proposed method is based on an improved particle swarm optimization (PSO) algorithm, including two phases. First, an inverted pyramid structure is employed to divide the solution space into a top-level solution space, such that the expected solution can be quickly located into a smaller range from bottom to top. Then, the spacial three search step (S-TSS) algorithm is applied to achieve an accurate result. The effectiveness of the proposed method is evaluated on a three-DOF micro stage with translational and rotational motions, where comparative studies with existing results are also conducted. The experimental results demonstrate significant improvement on algorithm efficiency without sacrificing the measurement accuracy, which indicates good potential for visual servo control of such micro stages with the proposed approach. [ABSTRACT FROM AUTHOR]

Surveying wide areas with only one camera is a typical scenario in surveillance and automotive applications. Ultra wide-angle fisheye cameras employed to that end produce video data with characteristics that differ significantly from conventional rectilinear imagery as obtained by perspective pinhole cameras. Those characteristics are not considered in typical image and video processing algorithms such as motion estimation, where translation is assumed to be the predominant kind of motion. This contribution introduces an adapted technique for use in block-based motion estimation that takes into the account the projection function of fisheye cameras and thus compensates for the non-perspective properties of fisheye videos. By including suitable projections, the translational motion model that would otherwise only hold for perspective material is exploited, leading to improved motion estimation results without altering the source material. In addition, we discuss extensions that allow for a better prediction of the peripheral image areas, where motion estimation falters due to spatial constraints, and further include calibration information to account for lens properties deviating from the theoretical function. Simulations and experiments are conducted on synthetic as well as real-world fisheye video sequences that are part of a data set created in the context of this paper. Average synthetic and real-world gains of 1.45 and 1.51 dB in luminance PSNR are achieved compared against conventional block matching. Furthermore, the proposed fisheye motion estimation method is successfully applied to motion compensated temporal resolution enhancement, where average gains amount to 0.79 and 0.76 dB. [ABSTRACT FROM AUTHOR]

Automated condition monitoring of pavements is seeing a paradigm shift in the twenty-first century, as the focus is increasingly shifting from visual to 3D imaging. The 3D measurement represents the ground truth, within the bounds of discretization of the imager. Hence, working with the 3D data and interpreting it is a lot easier. The 3D laser scanners are the current norm for range imaging. Laser scanners, although technically versatile and extremely accurate, are very expensive. Therefore, the need for alternative 3D imaging technologies is imperative, especially in the developing world context. The coming decade will see alternative technologies first target large pavement distresses, like potholes, before moving onto smaller defect types. In this regard, this is the first study that treats the stereo imaging of roads from a moving vehicle, for any distresses, and especially for pothole measurements. A fast stereo matching procedure is proposed. A preliminary keypoint matching estimates the global disparity between the stereo pair. The estimated disparity is used toward further keypoint and block matching procedure, by concentrating on smaller image regions. Images obtained from a moving vehicle are preprocessed to reduce the effects of image blur. Furthermore, algorithms to determine key critical metrological parameters, such as the area, depth, and volume, of a pothole are also developed. A benchmarking of the proposed system shows that the 3D measurements are within 3 mm for static situations and 5 mm when imaged from a moving vehicle. At 10-km/h vehicle speed, potholes are imaged to accuracies of −20% for volume, −15% for area, and −4% for depth, when compared to static imagery. [ABSTRACT FROM AUTHOR]

Coherent imaging systems are greatly affected by speckle noise, which makes visual analysis and features extraction a difficult task. In this paper, we propose a speckle suppression algorithm based on weighted nuclear norm minimization (WNNM) and Grey theory. First, we use logarithmic transformation to the noisy images such that the speckle noise is transformed into additive noise. Second, by matching the local blocks based on Grey theory, we will get approximate low-rank matrices grouped by the similar blocks of the reference patches. We then estimate the noise variance of the noisy images with the wavelet transform. Finally, we use WNNM method to denoise the image. The results show that our algorithm not only effectively improves the visual effect of the denoised image and preserves the local structure of the image better but also improves the objective indexes values of the denoised image. [ABSTRACT FROM AUTHOR]

A number of block-based image-denoising methods have been presented in the literature. Those methods, however, are generally adapted to denoising the Gaussian noise, and subsequently do not show good performance for denoising random-valued impulse, and salt-and-pepper noise. We propose an efficient block-based image-denoising method, which is devised specially for fast denoising of impulse noise. The method first constructs a set of array pointers to image blocks containing a specific pixel value at a specific location. With this scheme, finding of blocks similar to a given block can be done by considering only the blocks pointed by the pointers corresponding to the pixel values of the block without comparing all the blocks in the input image. The experimental results show that the proposed method can achieve superior denoising performance in terms of computational time and signal-to-noise ratio measure. [ABSTRACT FROM AUTHOR]

Speckle tracking echocardiography (STE) is a widespread method for calculating myocardial strains and estimating left ventricle function. Since echocardiographic clips are corrupted by speckle decorrelation noise, resulting in irregular, nonphysiological tissue displacement fields, smoothing is performed on the displacement data, affecting the strain results. Thus, strain results may depend on the specific implementations of 2-D STE, as well as other systems’ characteristics of the various vendors. A novel algorithm (called K-SAD) is introduced, which integrates the physiological constraint of smoothness of the displacement field into an optimization process. Simulated B-mode clips, modeling healthy and abnormal cases, were processed by K-SAD. Peak global and subendocardial longitudinal strains, as well as regional strains, were calculated. In addition, 410 healthy subjects were also processed. The results of K-SAD are compared with those of one of the leading commercial product. K-SAD provides global mid-wall strain values, as well as subendocardial and regional strain values, all in good agreement with the ground-truth-simulated phantom data. K-SAD peak global longitudinal systolic strain values for 410 healthy subjects are quite similar for the different regions: − 17.02 ± 4.02%, − 19.00 ± 3.45%, and − 19.72 ± 5.06% at the basal, mid, and apical regions, respectively. Improved performance under noisy conditions was demonstrated by comparing a subgroup of 40 subjects with the best image quality with the remaining 370 cohort: K-SAD provides statistically similar global and regional results for the two cohorts. Our study indicates that the sensitivity of strain values to speckle noise, caused by the post block-matching weighted smoothing, can be significantly reduced and accuracy enhanced by employing an integrated one-stage, physiologically constrained optimization process. [ABSTRACT FROM PUBLISHER]

In this paper, a recursive sliding transform (RST) algorithm is proposed for the fast implementation of the Walsh Hadamard Transform (WHT) on sliding windows. The proposed RST algorithm accelerates the sliding WHT process by recursively reducing the order of WHT. Theoretical analysis shows that the proposed RST algorithm requires only 1.33 additions per sample for each WHT basis vector regardless of the size or dimension of the basis vector. The computational requirement of the proposed RST algorithm is the lowest among existing fast sliding WHT algorithms. [ABSTRACT FROM PUBLISHER]

Today, many people in the world without any (or with little) knowledge about video recording, thanks to the widespread use of mobile devices (personal digital assistants, mobile phones, etc.), take videos. However, the unwanted movements of their hands typically blur and introduce disturbing jerkiness in the recorded sequences. Many video stabilization techniques have been hence developed with different performances but only fast strategies can be implemented on embedded devices. A fundamental issue is the overall robustness with respect to different scene contents (indoor, outdoor, etc.) and conditions (illumination changes, moving objects, etc.). In this paper, we propose a fast and robust image alignment algorithm for video stabilization purposes. Our contribution is twofold: a fast and accurate block-based local motion estimator together with a robust alignment algorithm based on voting. Experimental results confirm the effectiveness of both local and global motion estimators. [ABSTRACT FROM AUTHOR]

Many motion compensation algorithms are based on block matching. The quality of the block correlation depends on the validity of the brightness constancy assumption and the assumption of fixed translational motion within a block. These assumptions are invalid in areas with texture changes, noise, lighting changes, and rapid deformations. Smoothness priors should enforce stable estimates in these regions by propagating neighboring estimates, while preserving hard object boundaries (piecewise smoothness). Most motion estimation algorithms that successfully implement these constraints are computationally complex. In this paper, we show an intuitive and computationally efficient way to implement them within the framework of (real-time) recursive search, targeting consumer-market embedded devices with limited resources. [ABSTRACT FROM PUBLISHER]

Digital video stabilization enables to acquire video sequences without disturbing jerkiness by compensating unwanted camera movements. In this paper, we propose a novel fast image registration algorithm based on block matching. Unreliable motion vectors (i.e., not related with jitter movements) are properly filtered out by making use of ad-hoc rules taking into account local similarity, local “activity,” and matching effectiveness. Moreover, a temporal analysis of the relative error computed at each frame has been performed. Reliable information is then used to retrieve inter-frame transformation parameters. Experiments on real cases confirm the effectiveness of the proposed approach even in critical conditions. [ABSTRACT FROM PUBLISHER]

The candidate position subsampling technique (CPST) basically chooses candidates in a search window at a sampling rate. The 2:1 CPST chooses half the candidates, and then selects one or more candidates that are considered as to be close to the optimal motion vector before conducting a fine search. The fine search is conducted by checking four neighbors of the chosen candidate(s) referred to as winner(s). The CPST can be combined with a fast optimal block-matching algorithm, such as the multilevel successive elimination algorithm (MSEA), in order to reduce the number of computations used in rejecting the nonbest candidate. We propose a new 2:1 CPST fitted to the MSEA. The proposed algorithm adopts a new condition for the winner which helps to find the best candidate efficiently. Moreover, a fast motion estimation step is used to reduce the number of computations of the MSEA, and the peak signal-to-noise ratio (PSNR) compensation step is adopted to guarantee that the PSNR performance of the proposed algorithm is very close to that of the full search. Experimental results show that the proposed algorithm reduces the computational loads of the MSEA by 47.26% on average with only -0.027 dB PSNR degradation in the worst case. [ABSTRACT FROM AUTHOR]

In this letter, a new fast motion estimation (FME) algorithm capable of producing sub-sample motion vectors at low computational-complexity is proposed. Unlike existing FME algorithms, the proposed algorithm considers the low complexity sub-sample accuracy in designing the search pattern for FME. The proposed FME algorithm is designed in such a way that the block distortion measure is modeled as a parametric surface in the vicinity of the integer-sample motion vector; this modeling enables low computational-complexity sub-sample motion estimation (ME) without pixel interpolation. Experimental results on video test sequences show that the proposed FME algorithm reduces computational complexity of integer and sub-sample ME considerably compared with traditional methods at the cost of negligible performance degradation. [ABSTRACT FROM AUTHOR]

Abstract--Motion in modern video coders is estimated using a block matching algorithm that calculates the distance and direction of motion on a block-by-block basis. In this paper, a novel fast block-based motion estimation algorithm is proposed. This algorithm uses an efficient projection framework that bounds the distance between a template block and candidate blocks. Fast projection is performed using a family of highly efficient filter kernels-the gray-code kernels--requiring only 2 operations per pixel per kernel. The projection framework is combined with a rejection scheme which allows rapid rejection of candidate blocks that are distant from the template block. The tradeoff between computational complexity and quality of results can be easily controlled in the proposed algorithm; thus, it enables adaptivity to image content to further improve the results. Experiments show that the proposed adaptive algorithm outperforms other popular fast motion estimation algorithms. [ABSTRACT FROM AUTHOR]

Search point pattern-based fast block motion estimation algorithms provide significant speedup for motion estimation but usually suffer from being easily trapped in local minima. This may lead to low robustness in prediction accuracy particularly for video sequences with complex motions. This problem is especially serious in one-at-a-time search (OTS) and block-based gradient descent search (BBGDS), which provide very high speedup ratio. A multipath search using more than one search path has been proposed to improve the robustness of BBGDS but the computational requirement is much increased. To tackle this drawback, a novel directional gradient descent search (DGDS) algorithm using multiple OTSs and gradient descent searches on the error surface in eight directions is proposed in this letter. The search point patterns in each stage depend on the minima found in these eight directions, and thus the global minimum can be traced more efficiently. In addition, a fast version of the DGDS (FDGDS) algorithm is also described to further improve the speed of DGDS. Experimental results show that DGDS reduces computation load significantly compared with the well-known fast block motion estimation algorithms. Moreover, FDGDS can achieve faster speedup com- pared with the UMHexagonS algorithm in H.264/AVC implementation while maintaining very similar rate-distortion performance. [ABSTRACT FROM AUTHOR]

Block matching based motion estimation employs conventionally pixel-based sum of absolute differences (SAD) metric for distortion measure. Many fast matching mechanisms have been developed to lower the computational load for block matching, such as those exploiting block feature based or attribute-based SAD calculations. By examining a subblock-sum-based SAD measure, where the subblock-sums can be considered to be intra-subblock features, we propose both intra- and inter-subblock features based SAD measure for more effective block matching in terms of achieving better coding efficiency. Interestingly, the proposed feature based SAD measure can be interpreted as subblock-based sum of absolute Hadamard transformed differences (SATD). The new features can be constructed in a multilevel structure, which may provide a flexible and scalable means for a tradeoff between computation load and coding efficiency. Encoding results are shown to compare the proposed scheme against other relevant feature based SAD measures. [ABSTRACT FROM AUTHOR]

The motion of the carotid artery wall can quantitatively be estimated from sequences of B-mode ultrasound images. In this paper, the effects of dynamic range (DR) and persistence, along with that of image normalization, were studied, in an attempt to suggest optimal values for reliable motion analysis. Image sequences were recorded using four different values for DR, i.e., 0, 48, 66, and 90 dB, and three different values for persistence, i.e., 0, 5.6, and 50. Radial and axial displacements, as well as the correlation coefficients (CCs), were estimated using block matching from recordings with durations of about 3 s. The variances of radial and axial displacements were not significantly affected by changes in DR and persistence, The mean value of the CC, which is an index of the reliability of motion analysis, was also not significantly affected by these settings. However, an increase in persistence increased the delays between peak radial displacements and cardiac systole. Image normalization did not affect the results of motion analysis. It is suggested that high values of DR (66 or 90dB) and low values of persistence (0 or 5.6) are used for motion analysis based on block matching. [ABSTRACT FROM AUTHOR]

Abstract-We present a bioinspired model for detecting spatiotemporal features based on artificial retina response models. Event-driven processing is implemented using four kinds of cells encoding image contrast and temporal information. We have evaluated how the accuracy of motion processing depends on local contrast by using a multiscale and rank-order coding scheme to select the most important cues from retinal inputs. We have also developed some alternatives by integrating temporal feature results and obtained a new improved bioinspired matching algorithm with high stability, low error and low cost. Finally, we define a dynamic and versatile multimodal attention operator with which the system is driven to focus on different target features such as motion, colors, and textures. [ABSTRACT FROM AUTHOR]

Center-biased fast motion estimation algorithms, e.g., block-based gradient descent search and diamond search, can perform. much better than coarse-to-fine search algorithms, such as 2-D logarithmic search and three-step search. The latter type of algorithms, however, is more suitable for handling large motion content. To combine the advantages of both types of algorithms, an adaptive algorithm performing search patterns switching (SPS) is proposed in this paper. The proposed SPS algorithm classifies the motion content of a block using a simple yet efficient motion content classifier called error descent rate. Unlike other classifiers with heavy overhead, this classifier requires only the searching of a few points in the search window and then a division operation. Experimental results show that the proposed SPS algorithm is very robust. [ABSTRACT FROM AUTHOR]

Motion estimation (ME) plays an important role in modern video coders since it consumes approximately 60-80% of the entire encoder's computations. In this paper, three novel techniques are proposed to effectively speed up the ME process. First, a smart prediction technique for effectively deciding an initial search center is proposed. Second, a zero motion prejudgment technique is proposed to accurately decide whether the pre-estimated ISC can be considered as a best match motion vector (MV) and consequently save the required computations for the MV refinement process. Finally, a variable padding pixels ME technique is proposed to adaptively determine the number of padding pixels required for the search window for more computational cost savings. The three techniques are combined and applied to the block-based ME for a superior computational complexity savings in the ME process. The performance of the proposed techniques is tested in both the pixel domain ME and the frequency domain ME in terms of their quantitative visual quality (peak signal-to-noise ratio, PSNR), their computational complexity, and their bit rate. Experimental results demonstrate that the proposed fast ME technique is able to achieve approximately a 99.4% reduction in ME time compared to the conventional full search block-based ME (FSSBB-ME) with negligible degradation in both the PSNR and the bit rate. Additionally, the experimental results prove the effectiveness of the proposed techniques if they are combined with any blockbased ME technique such as the fast extended diamond enhanced predictive zonal search. Experimental results also demonstrate that there is at least an additional savings of 72% in ME time. using the conventional discrete cosine transform phase correlation ME (DCT-PC-ME) in the frequency domain compared to the conventional FSBB-ME technique in pixel domain. Compared to the conventional DCT-PC-ME, applying the proposed novel techniques to the DCT-PC-ME saves up to 89% in ME time. [ABSTRACT FROM AUTHOR]