scholarly journals Building a modified block matching kernel based on Wave Atom transform for efficient image denoising

2021 ◽  
Author(s):  
Zouhair Mbarki ◽  
Chiraz Ben Jabeur Seddik ◽  
Hassene Seddik
Keyword(s):  
Image Denoising ◽  
Block Matching ◽  
Modified Block ◽  
Wave Atom Transform

Image Denoising Using Sparse Representation and Principal Component Analysis

2021 ◽  
pp. 2250033
Author(s):  
Maryam Abedini ◽  
Horriyeh Haddad ◽  
Marzieh Faridi Masouleh ◽  
Asadollah Shahbahrami
Keyword(s):  
Principal Component Analysis ◽  
Sparse Representation ◽  
Image Denoising ◽  
Matching Pursuit ◽  
Signal To Noise Ratio ◽  
Principal Component ◽  
Structural Similarity ◽  
Component Analysis ◽  
Block Matching ◽  
Discrete Wavelet

This study proposes an image denoising algorithm based on sparse representation and Principal Component Analysis (PCA). The proposed algorithm includes the following steps. First, the noisy image is divided into overlapped [Formula: see text] blocks. Second, the discrete cosine transform is applied as a dictionary for the sparse representation of the vectors created by the overlapped blocks. To calculate the sparse vector, the orthogonal matching pursuit algorithm is used. Then, the dictionary is updated by means of the PCA algorithm to achieve the sparsest representation of vectors. Since the signal energy, unlike the noise energy, is concentrated on a small dataset by transforming into the PCA domain, the signal and noise can be well distinguished. The proposed algorithm was implemented in a MATLAB environment and its performance was evaluated on some standard grayscale images under different levels of standard deviations of white Gaussian noise by means of peak signal-to-noise ratio, structural similarity indexes, and visual effects. The experimental results demonstrate that the proposed denoising algorithm achieves significant improvement compared to dual-tree complex discrete wavelet transform and K-singular value decomposition image denoising methods. It also obtains competitive results with the block-matching and 3D filtering method, which is the current state-of-the-art for image denoising.

Analysis and Implementation of MPEG-4 Motion Codec

2012 ◽  
Vol 268-270 ◽  
pp. 1667-1670
Author(s):  
Yun Peng Liu ◽  
Ren Fang Wang ◽  
Jin Li
Keyword(s):  
Motion Estimation ◽  
Motion Compensation ◽  
Block Matching ◽  
Modified Block

Motion codec is the key technique of MPEG-4 VM. The motion encoding and decoding use “Unrestricted Motion Estimation”, “Advanced Prediction”, “Overlapped Motion Compensation”, in addition, the particular “Repetitive Padding”, and “Modified Block Matching “ are added for MPEG-4. At the same time, some questions during motion are explained carefully, and some new viewpoints are brought forward.

scholarly journals Image Denoising in Spatial and Transform Domains

2021 ◽  
Author(s):  
Mina Sharifymoghaddam
Keyword(s):  
Image Denoising ◽  
Signal To Noise Ratio ◽  
Similarity Index ◽  
Weighting Scheme ◽  
Block Matching ◽  
Hard Thresholding ◽  
Weighted Version ◽  
Nonlocal Means ◽  
Processing Step ◽  
Index Measure

Image denoising is an inseparable pre-processing step of many image processing algorithms. Two mostly used image denoising algorithms are Nonlocal Means (NLM) and Block Matching and 3D Transform Domain Collaborative Filtering (BM3D). While BM3D outperforms NLM on variety of natural images, NLM is usually preferred when the algorithm complexity is an issue. In this thesis, we suggest modified version of these two methods that improve the performance of the original approaches. The conventional NLM uses weighted version of all patches in a search neighbourhood to denoise the center patch. However, it can include some dissimilar patches. Our first contribution, denoted by Similarity Validation Based Nonlocal Means (NLM-SVB), eliminates some of those unnecessary dissimilar patches in order to improve the performance of the algorithm. We propose a hard thresholding pre-processing step based on the exact distribution of distances of similar patches. Consequently, our method eliminates about 60% of dissimilar patches and improves NLM in terms of Peak Signal to Noise Ratio (PSNR) and Stracuteral Similarity Index Measure (SSIM). Our second contribution, denoted by Probabilistic Weighting BM3D (PW-BM3D), is the result of our thorough study of BM3D. BM3D consists of two main steps. One is finding a basic estimate of the noiseless image by hard thresholding coefficients. The second one is using this estimate to perform wiener filtering. In both steps the weighting scheme in the aggregation process plays an important role. The current weighting process depends on the variance of retrieved coefficients after denoising which results in a biased weighting. In PW-BM3D, we propose a novel probabilistic weighting scheme which is a function of the probability of similarity of noiseless patches in each 3D group. The results show improvement over BM3D in terms of PSNR for an average of about 0.2dB.

scholarly journals Image denoising by bounded block matching and 3D filtering

2010 ◽  
Vol 90 (9) ◽  
pp. 2778-2783 ◽  
Author(s):  
Qian Chen ◽  
Dapeng Wu
Keyword(s):  
Image Denoising ◽  
Block Matching

Evolutionary Fuzzy Block-Matching-Based Camera Raw Image Denoising

2017 ◽  
Vol 47 (9) ◽  
pp. 2862-2871 ◽  
Author(s):  
Chin-Chang Yang ◽  
Shu-Mei Guo ◽  
Jason Sheng-Hong Tsai
Keyword(s):  
Image Denoising ◽  
Block Matching

Hyperspectral complex-domain image denoising: cube complex-domain BM3D (CCDBM3D) algorithm

2020 ◽  
Vol 2020 (10) ◽  
pp. 179-1-179-7
Author(s):  
Vladimir Katkovnik ◽  
Mykola Ponomarenko ◽  
Karen Egiazarian ◽  
Igor Shevkunov ◽  
Peter Kocsis
Keyword(s):  
Image Denoising ◽  
High Efficiency ◽  
State Of The Art ◽  
Block Matching ◽  
Complex Domain ◽  
Simulation Experiments ◽  
Cube Complex ◽  
Spatial Dimensions ◽  
Phase Amplitude ◽  
Complex Valued

We consider hyperspectral phase/amplitude imaging from hyperspectral complex-valued noisy observations. Block-matching and grouping of similar patches are main instruments of the proposed algorithms. The search neighborhood for similar patches spans both the spectral and 2D spatial dimensions. SVD analysis of 3D grouped patches is used for design of adaptive nonlocal bases. Simulation experiments demonstrate high efficiency of developed state-of-the-art algorithms.

Medical image denoising based on improving K-SVD and block-matching 3D filtering

2016 ◽  
Author(s):  
Jing Bai ◽  
Yanchao Sun ◽  
Ting Fan ◽  
Shu Song ◽  
Xiangrong Zhang
Keyword(s):  
Image Denoising ◽  
Medical Image ◽  
Block Matching
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