Iterative Image Restoration using a Non-Local Regularization Function and a Local Regularization Operator

2006 ◽  
Author(s):  
Feng Xue ◽  
Quan-sheng Liu ◽  
Wei-hong Fan
Keyword(s):  
Image Restoration ◽  
Local Regularization ◽  
Regularization Operator ◽  
Non Local

scholarly journals Self-Similarity Sparse Representation for Image Restoration

2019 ◽  
Vol 8 (2S11) ◽  
pp. 1063-1067
Keyword(s):  
Image Restoration ◽  
Mean Square Error ◽  
Gaussian Noise ◽  
Local Similarity ◽  
Mean Square ◽  
Regularization Term ◽  
Local Regularization ◽  
Degraded Image ◽  
Local Patterns ◽  
Non Local

Image restoration aims to restore an image from a degraded image. The degradation may occur during image acquisition or image transmission. Image degradation lowers the quality of the image. In this paper additive Gaussian noise is considered for degrading the original image. For restoring the image from degraded image the proposed method used both local and non-local similarity patterns. The restoration problem is modeled with regression model. Two regularization terms are considered for representing prior image information. One regularization term is for local patterns and other is for non-local similarity patterns. The additive local regularization term is used to restore the edges. The non-local regularization term works best for local smoothness and edge information will be lost. The proposed algorithm took a clean image of size 256x256 and added with Gaussian noise with different levels of noise levels. A self-adaptive dictionary is trained for a particular window of image with local and non-local patterns and stacked to three dimensional matrix. The patch size considered for training the dictionary is 10x10. For restoring each patch it searches best atoms form the trained dictionary. The efficiency of the algorithm is estimated by parameters mean square error, root mean square error, PSNR and FSIM. The algorithm is also tested for different images like cameraman, house, Barbara, Lena and parrot. The proposed algorithm is tested with conventional algorithms. .

Combined non-local and multi-resolution sparsity prior in image restoration

2012 ◽  
Author(s):  
Jan Aelterman ◽  
Bart Goossens ◽  
Hiep Luong ◽  
Jonas De Vylder ◽  
Aleksandra Pizurica ◽  
...  
Keyword(s):  
Image Restoration ◽  
Non Local

Non-local sparse models for image restoration

2009 ◽  
Author(s):  
Julien Mairal ◽  
Francis Bach ◽  
Jean Ponce ◽  
Guillermo Sapiro ◽  
Andrew Zisserman
Keyword(s):  
Image Restoration ◽  
Sparse Models ◽  
Non Local

Non-local Meets Global: An Integrated Paradigm for Hyperspectral Image Restoration

2020 ◽  
pp. 1-1
Author(s):  
Wei He ◽  
Quanming Yao ◽  
Chao Li ◽  
Naoto Yokoya ◽  
Qibin Zhao ◽  
...  
Keyword(s):  
Image Restoration ◽  
Hyperspectral Image ◽  
Non Local

Group Sparsity Residual Constraint With Non-Local Priors for Image Restoration

2020 ◽  
Vol 29 ◽  
pp. 8960-8975
Author(s):  
Zhiyuan Zha ◽  
Xin Yuan ◽  
Bihan Wen ◽  
Jiantao Zhou ◽  
Ce Zhu
Keyword(s):  
Image Restoration ◽  
Group Sparsity ◽  
Non Local

scholarly journals Regularized Non-local Total Variation and Application in Image Restoration

2017 ◽  
Vol 59 (2) ◽  
pp. 296-317 ◽  
Author(s):  
Zhi Li ◽  
François Malgouyres ◽  
Tieyong Zeng
Keyword(s):  
Image Restoration ◽  
Total Variation ◽  
Non Local
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