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Noisy image block matching based on dissimilarity measure in discrete cosine transform domain
摘要： In this paper, the problem of image block similarity measuring in noisy environment is considered. In different practical applications often is necessary to ?nd groups of similar image blocks within an ample search area. In such situation, the full search algorithm is very slow; apart, its accuracy is low due to the presence of noise. New algorithms for similar image block matching in noisy environment are presented. The algorithms are based on the dissimilarity measure calculated as the distance between image patches in the discrete cosine transform domain. The proposed algorithms perform the hierarchical search for the similar image blocks and hereby have a reduced complexity in comparison to the full search algorithm. Adjusting the radius of the distance calculation for spectral coef?cient matching, the characteristics of the block matching algorithm can easily be adjusted to obtain a better accuracy of the matched block group. A higher accuracy is obtained using the local adaptation of the radius for the distance calculation outperforming the existing algorithms used to ?nd groups of similar blocks in different applications, such as image noise ?ltering and image clustering. The performance of the different block matching algorithms were evaluated on the base of the proposed accuracy measure that uses as a reference the list of patches obtained with the full search algorithm in the absence of noise.
关键词： Dissimilarity measure,hierarchical search,local adaptation,noisy image block matching,discrete cosine transform
更新于2025-09-23 15:21:01
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Disparities selection controlled by the compensated image quality for a given bitrate
摘要： A stereoscopic image consists of two views rendering a depth sense. Indeed each eye is constrained to look at one view, and the small objects displacements across the two views are interpreted as an indication of depth. These displacements are exploited as speci?c inter-view redundancies from a compression viewpoint. The classical still compression scheme, called disparity-compensated compression scheme, compresses one view independently of the second view, and a block-based disparity map modeling the displacements is losslessly compressed. The difference between the original view and its disparity predicted view is then compressed and used by the decoder to compute the compensated view to improve the disparity predicted view. However, a proof of concept work has already shown that selecting disparities according to the compensated view, instead of the predicted view, yields increased rate-distortion performance. This paper derives from the JPEG-coder, a disparity-dependent analytic expression of the distortion induced by the compensated view. This expression is embedded into an algorithm with a reasonable numerical complexity approaching the performance obtained with the proof of concept work. The proposed algorithm, called fast disparity-compensated block matching algorithm, provides at the same bitrate an average performance increase as compared to the classical stereoscopic image coding schemes.
关键词： Stereoscopic image,Compression,Block matching algorithm,Disparity compensation,JPEG-distortion
更新于2025-09-23 15:19:57
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[IEEE 2019 IEEE International Instrumentation and Measurement Technology Conference (I2MTC) - Auckland, New Zealand (2019.5.20-2019.5.23)] 2019 IEEE International Instrumentation and Measurement Technology Conference (I2MTC) - A Method for Three-Dimensional Measurements Using Widely Angled Stereoscopic Cameras
摘要： Computer vision technologies have become popular tools for performing non-contact measurements. Stereoscopic systems have been used in several applications for length and geometry measurements. Three-dimensional (3D) reconstruction is an essential part of performing 3D measurements. A variety of methods have been developed for 3D reconstruction in stereoscopic systems. Block matching methods are considered as the most suitable option for 3D measurements, but they require the views to be similar for the cameras of a stereoscopic system. To satisfy this need, the cameras of a stereoscopic system should have small angles between their optical axes on the object’s surface. However, it is not always feasible nor desirable to arrange cameras in this way for some applications. We have proposed a new method to address this restriction. Our method uses an initial transform between the images from two cameras to make the views similar. Points on the transformed images are used as initial estimates of matched points in the two camera views. The points are then matched between the two images using an accurate subpixel image registration algorithm. The new method was tested using an object with known dimensions. The maximum measurement error achieved was 0.05 mm with a standard deviation of 0.09 mm for 10 measurements of a 12 mm length. The high accuracy of this method makes it a suitable option for applications that require reliable 3D measurements.
关键词： subpixel,3D reconstruction,block matching,stereoscopic measurements,wide base line cameras,image registration
更新于2025-09-16 10:30:52
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Depth Information Enhancement using Block Matching and Image Pyramiding Stereo Vision Enabled RGB-D Sensor
摘要： 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 RealSenseTM. 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.
关键词： disparity estimation,Block matching,stereo vision,image pyramiding,RGB-D sensor,depth sensing
更新于2025-09-16 10:30:52
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Wavelet Transforms, Contourlet Transforms and Block Matching Transforms for Denoising of Corrupted Images via Bi-shrink Filter
摘要： Image Denoising refers to the recovery of an image that has been corrupted by noise due to poor quality of image acquisition and transmission. Accordingly, there is a need to reduce the noise present in the image as a consequence of the denoised image formed. This paper presents Image denoising using Wavelet transforms, Contourlet transforms and Block Matching Transforms governed by bivariate shrinkage (Bi-shrink) filter techniques. The Wavelet transform uses up-sampling, down-sampling, low pass filter and high pass filter to perform denoising operation, the Contourlet transform uses up-sampling, down-sampling, low pass filter and high pass filter and directional filter banks to perform denoising operation, the Block Matching Transform uses Haar Transforms, Discrete cosine transforms and Karhunen Loeve transform to perform denoising operation. The performance of wavelet transforms, Contourlet transforms and Block Matching Transforms are evaluated for Reference images (such as towers, shades and ruler images) corrupted by gaussian noise and salt and pepper noise, by computing two error metrics Peak Signal to Noise Ratio (PSNR) and Execution Time (ET) with help of shrinkage function. Programming these using MATLAB R2014a by exploring its wavelet transform, Contourlet transform, image processing and signal processing toolboxes and the values are presented in tabular forms and discussed in the section 6. In this paper the block matching haar discrete cosine transform is proposed for denoising of images (especially for those images possessing detailed textures) that works through haar transform and discrete cosine transform outstrips the basic transform discrete wavelet transform and semi translation invariant contourlet transform. For the images corrupted by Gaussian noise and denoised by the proposed transform outstrips the basic transform “Discrete Wavelet Transform by PSNR=6.71 dB, ET=25.89 sec” and “Semi Translation Invariant Contourlet Transforms by PSNR=5.49 dB, ET=5.89 sec”. For the images corrupted by Salt and Pepper noise and denoised by the proposed transform outstrips the basic transform “Discrete Wavelet Transform by PSNR=21.15 dB, ET= 0.27 sec” and “Semi Translation Invariant Contourlet Transforms by PSNR=20.05 dB, ET= 5.80 sec”. In this paper Block Matching Haar Discrete cosine transform is proposed to overcome the limitations of wavelet transforms and Contourlet transforms, hence to attain the trade-off between high peak signal to noise ratio and less execution time. Results and Discussion section illustrates the efficacy of the proposed transform in terms of peak signal to noise ratio, execution time and visual quality of images.
关键词： Wavelet Transforms,Contourlet Transforms,Bi-variate Shrinkage and Image Denoising,Block Matching Transforms
更新于2025-09-11 14:15:04
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Image noise reduction based on adaptive thresholding and clustering
摘要： In this paper, we present a novel image denoising method based on adaptive thresholding and k-means clustering. In this method, we adopt the adaptive thresholding technique as an alternative to the traditional hard-thresholding of the block-matching and 3D filtering (BM3D) method. This technique has a high capacity to adapt and change according to the amount of the noise. More precisely, in our method the soft-thresholding is applied to the areas with heavy noise, on the contrary the hard-thresholding is applied to the areas with slight noise. Based on the adaptation and stability of the adaptive thresholding, we can achieve optimal noise reduction and maintain the high spatial frequency detail (e.g. sharp edges). Owing to the capacity of k-means clustering in terms of finding the relevant candidate-blocks, we adopt this clustering at the last estimate to partition the denoised image into several regions and identify the boundaries between these regions. Applying k-means clustering will allow us to force the block matching to search within the region of the reference block, which in turn will lead to minimize the risk of finding poor matching. The main reason of applying the K-means clustering method on the denoised image and not on the noised image is specifically due to the flaw of accuracy in detecting edges in the noisy image. Experimental results demonstrate that the new algorithm consistently outperforms other reference methods in terms of visual quality, Peak Signal-to-Noise Ratio (PSNR) and Structural Similarity Index (SSIM). Furthermore, in the proposed algorithm the time consumption of the image denoising is less than that in the other reference algorithms.
关键词： Candidate-blocks,Block matching,Adaptive thresholding,Hard-thresholding,Reference-blocks,K-means clustering,Soft-thresholding
更新于2025-09-09 09:28:46
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[IEEE 2018 15th International Multi-Conference on Systems, Signals & Devices (SSD) - Yassmine Hammamet, Tunisia (2018.3.19-2018.3.22)] 2018 15th International Multi-Conference on Systems, Signals & Devices (SSD) - Real Time Stereo Matching Using Two Step Zero-Mean SAD and Dynamic Programing
摘要： Dense depth map extraction is a dynamic research field in a computer vision that tries to recover three-dimensional information from a stereo image pair. A large variety of algorithms has been developed. The local methods based on block matching that are prevalent due to the linear computational complexity and easy implementation. This local cost is used on global methods as graph cut and dynamic programming in order to reduce sensitivity to local to occlusion and uniform texture. This paper proposes a new method for matching images based on a two-stage of block matching as local cost function and dynamic programming as energy optimization approach. In our work introduce the two stage of the zero-mean sum of absolute differences (ZSAD) combined with dynamic programming: the smoothness and ordering constraints are used to optimize correspondences. Stereo matching accuracy and runtime are the fundamental metrics to evaluate the stereo matching methods. The real-time has become a reality through the complexity reduction of the calculation and the use of parallel high-performance graphics hardware. In this paper we evaluate the developed method on using Middlebury stereo benchmark and, we propose a GPU CUDA implementation in order to accelerate our algorithm and reach the real time.
关键词： dynamic programming,stereo matching,GPU,CUDA implementation,cost function,block matching
更新于2025-09-04 15:30:14
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[IEEE 2018 - 3DTV-Conference: The True Vision - Capture, Transmission and Display of 3D Video (3DTV-CON) - Helsinki (2018.6.3-2018.6.5)] 2018 - 3DTV-Conference: The True Vision - Capture, Transmission and Display of 3D Video (3DTV-CON) - A NOVEL DISPARITY-ASSISTED BLOCK MATCHING-BASED APPROACH FOR SUPER-RESOLUTION OF LIGHT FIELD IMAGES
摘要： Currently, available plenoptic imaging technology has limited resolution. That makes it challenging to use this technology in applications, where sharpness is essential, such as film industry. Previous attempts aimed at enhancing the spatial resolution of plenoptic light field (LF) images were based on block and patch matching inherited from classical image super-resolution, where multiple views were considered as separate frames. By contrast to these approaches, a novel super-resolution technique is proposed in this paper with a focus on exploiting estimated disparity information to reduce the matching area in the super-resolution process. We estimate the disparity information from the interpolated LR view point images (VPs). We denote our method as light field block matching super-resolution. We additionally combine our novel super-resolution method with directionally adaptive image interpolation from [1] to preserve sharpness of the high-resolution images. We prove a steady gain in the PSNR and SSIM quality of the super-resolved images for the resolution enhancement factor 8x8 as compared to the recent approaches and also to our previous work [2].
关键词： Light Field Image Super-Resolution,Block Matching,4D Imaging,Resolution Enhancement
更新于2025-09-04 15:30:14