Plug-and-Play video reconstruction using sparse 3D transform-domain block matching

2021 ◽  
Vol 32 (3) ◽  
Author(s):  
Vahid Khorasani Ghassab ◽  
Nizar Bouguila
Keyword(s):  
Block Matching ◽  
Transform Domain ◽  
Plug And Play ◽  
Domain Block ◽  
Video Reconstruction

scholarly journals Multi-focus image fusion based on block matching in 3D transform domain

2018 ◽  
Vol 29 (2) ◽  
pp. 415-428 ◽  
Author(s):  
Dongsheng YANG ◽  
◽  
Shaohai HU ◽  
Shuaiqi LIU ◽  
Xiaole MA ◽  
...  
Keyword(s):  
Image Fusion ◽  
Block Matching ◽  
Transform Domain ◽  
Focus Image

Noisy image block matching based on dissimilarity measure in discrete cosine transform domain

2019 ◽  
Vol 36 (4) ◽  
pp. 3169-3176 ◽  
Author(s):  
Miguel de Jesús Martínez Felipe ◽  
Edgardo Manuel Felipe Riverón ◽  
Jesús Alberto Martínez Castro ◽  
Oleksiy Pogrebnyak
Keyword(s):  
Discrete Cosine Transform ◽  
Dissimilarity Measure ◽  
Block Matching ◽  
Transform Domain ◽  
Noisy Image ◽  
Image Block ◽  
Cosine Transform

Fractal Coding of Video Sequence by Circular Prediction Mapping

Fractals ◽  
1997 ◽  
Vol 05 (supp01) ◽  
pp. 75-88 ◽  
Author(s):  
Chang-Su Kim ◽  
Sang-Uk Lee
Keyword(s):  
Video Sequence ◽  
Prediction Method ◽  
Temporal Correlation ◽  
Block Matching ◽  
Systematic Analysis ◽  
Block Matching Algorithm ◽  
Previous Frame ◽  
Real Video ◽  
Domain Block ◽  
Collage Theorem

We propose a novel algorithm for fractal video sequence coding, based on the Circular Prediction Mapping (CPM), in which each range block is approximated by a domain block in the circularly previous frame. In our approach, the size of the domain block is set to be same as that of the range block for exploiting the high temporal correlation between the adjacent frames, while most other fractal coders use the domain block larger than the range block. Therefore the domain-range mapping in the CPM becomes similar to the block matching algorithm in the motion compensation techniques, and the advantages from this similarity are discussed. Also the modified collage theorem, which yields better prediction method for the CPM than the conventional collage theorem, is provided by linear systematic analysis. The computer simulation results on real video-conferencing image sequences demonstrate that the average compression ratios ranging from 60 to 70 can be obtained with good subjective quality.

RL Algorithm for Passive Millimeter Wave Imaging Based on BM3D

2013 ◽  
Vol 411-414 ◽  
pp. 1155-1158 ◽  
Author(s):  
Yi Ming Niu ◽  
Can Cui ◽  
Guo Yang ◽  
Wen Wu
Keyword(s):  
Millimeter Wave ◽  
Imaging System ◽  
Block Matching ◽  
Transform Domain ◽  
Nonlinear Method ◽  
Millimeter Wave Imaging ◽  
Wave Imaging ◽  
Influence Of Noise ◽  
Modified Algorithm ◽  
Passive Millimeter Wave Imaging

In a passive millimeter wave (PMMW) imaging system, the resolution of the acquired image is limited by the antenna size. The Richardson—Lucy (RL) algorithm is a simple and nonlinear method, which can improve the resolution of the image. However, when the noise can not be neglected, it is difficult for RL algorithm to get good restoration of the corrupted image. To the best of our knowledge, the block-matching with 3D transform domain collaborative filtering (BM3D) algorithm achieves very good performance in image de-noising. In order to improve the resolution of passive millimeter wave images, a RL imaging algorithm for passive millimeter wave based on BM3D is proposed in this paper. The modified algorithm effectively reduces the influence of noise on RL algorithm by using de-noise algorithm based on BM3D. Experimental results demonstrate that the proposed algorithm improves the performance of RL algorithm. Furthermore, the algorithm can be easily implemented for passive millimeter wave imaging．

scholarly journals Iterative based image and video denoising by fractional block matching and transform domain filtering

2021 ◽  
Author(s):  
Amir Mehdizadeh Hemat Abadi ◽  
Mohammad Reza Hosseiny Fatemi
Keyword(s):  
Image Denoising ◽  
Gaussian Noise ◽  
Additive White Gaussian Noise ◽  
Weighted Average ◽  
Block Matching ◽  
Objective Measures ◽  
Transform Domain ◽  
Video Denoising ◽  
Simulation Results ◽  
Fractional Motion Estimation

This paper presents an iterative algorithm for image and video denoising which is based on fractional block-matching and transform domain filtering. We propose fractional motion estimation technique to find the most accurate similar blocks for each block of an image which improves sparsity enabling effective image denoising. By taking the advantage of blocks similarity and wavelet transform domain filtering along with weighted average function (WAF) in an iterative based manner, we achieve a higher level of sparsity and a better exploiting of blocks similarity redundancies of noisy images that increase the chance of preserving details and edges in the restored image. Since our algorithm is iterative, we can tradeoff between image denoising degree and computational complexity. In addition, we develop a video denoising algorithm based on the proposed image denoising algorithm. The simulation results of images and videos contaminated by additive white Gaussian noise demonstrate that our algorithm substantially achieves better denoising performance compared with previously published algorithms in terms of subjective and objective measures.

scholarly journals Block-Matching Based Multifocus Image Fusion

2015 ◽  
Vol 2015 ◽  
pp. 1-7
Author(s):  
Feng Zhu ◽  
Yingkun Hou ◽  
Jingyu Yang
Keyword(s):  
Image Fusion ◽  
Discrete Cosine Transform ◽  
Weighted Average ◽  
Objective Evaluation ◽  
Block Matching ◽  
Experimental Results ◽  
Transform Domain ◽  
Fusion Method ◽  
Spatial Methods ◽  
Image Fusion Method

A new multifocus image fusion method is proposed. Two image blocks are selected by sliding the window from the two source images at the same position, discrete cosine transform (DCT) is implemented, respectively, on these two blocks, and the alternating component (AC) energy of these blocks is then calculated to decide which is the well-focused one. In addition, block matching is used to determine a group of image blocks that are all similar to the well-focused reference block. Finally, all the blocks are returned to their original positions through weighted average. The weight is decided with the AC energy of the well-focused block. Experimental results demonstrate that, unlike other spatial methods, the proposed method effectively avoids block artifacts. The proposed method also significantly improves the objective evaluation results, which are obtained by some transform domain methods.

Fast image domain fractal compression by DCT domain block matching

1995 ◽  
Vol 31 (11) ◽  
pp. 869-870 ◽  
Author(s):  
B.E. Wohlberg ◽  
G. de Jager
Keyword(s):  
Block Matching ◽  
Image Domain ◽  
Fractal Compression ◽  
Dct Domain ◽  
Domain Block

Labmat: Learned Feature-Domain Block Matching For Image Restoration

2021 ◽  
Author(s):  
Shijun Liang ◽  
Berk Iskender ◽  
Bihan Wen ◽  
Saiprasad Ravishankar
Keyword(s):  
Image Restoration ◽  
Block Matching ◽  
Domain Block
Sign in / Sign up

Export Citation Format

Share Document