Spatially Adaptive Total Variation Regularization Model for Interdisciplinary Imaging Recovery

2016 ◽  
Author(s):  
Guanghua Gong ◽  
Hongming Zhang ◽  
Minyu Yao
Keyword(s):  
Total Variation ◽  
Total Variation Regularization ◽  
Spatially Adaptive ◽  
Adaptive Total Variation

scholarly journals Semiblind Image Deconvolution with Spatially Adaptive Total Variation Regularization

2014 ◽  
Vol 2014 ◽  
pp. 1-8 ◽  
Author(s):  
Yaduan Ruan ◽  
Houzhang Fang ◽  
Qimei Chen
Keyword(s):  
Total Variation ◽  
Spatial Information ◽  
Total Variation Regularization ◽  
Image Deconvolution ◽  
Split Bregman ◽  
Edge Information ◽  
Spatially Adaptive ◽  
Comparative Results ◽  
Simulated Images ◽  
Adaptive Total Variation

A semiblind image deconvolution algorithm with spatially adaptive total variation (SATV) regularization is introduced. The spatial information in different image regions is incorporated into regularization by using the edge indicator called difference eigenvalue to distinguish flat areas from edges. Meanwhile, the split Bregman method is used to optimize the proposed SATV model. The proposed algorithm integrates the spatial constraint and parametric blur-kernel and thus effectively reduces the noise in flat regions and preserves the edge information. Comparative results on simulated images and real passive millimeter-wave (PMMW) images are reported.

An Enhanced NAS-RIF Algorithm for Blind Image Restoration Based on Total Variation Regularization

2013 ◽  
Vol 423-426 ◽  
pp. 2522-2525
Author(s):  
Xin Ke Li ◽  
Chao Gao ◽  
Yong Cai Guo ◽  
Yan Hua Shao
Keyword(s):  
Image Restoration ◽  
Total Variation ◽  
Signal To Noise Ratio ◽  
Random Noise ◽  
Tikhonov Regularization Method ◽  
Total Variation Regularization ◽  
Inverse Filtering ◽  
Blind Image Restoration ◽  
Spatially Adaptive ◽  
Blind Image

In order to improve the quality of blind image restoration, we propose an algorithm which combines Non-negativity and Support constraint Recursive Inverse Filtering (NAS-RIF) and adaptive total variation regularization. In the proposed algorithm, the total variation regularization constraint term is added in the NAS-RIF algorithm cost function. The majorization-minimization approach and conjugate gradient iterative algorithm are adopted to improve the convergence speed. We do the simulation experiments for the blurred classic test image which is added additive random noise. Experimental results show that the restoration effect of our algorithm is better than the spatially adaptive Tikhonov regularization method and the NAS-RIF spatially adaptive regularization algorithm, while the value of improvement of signal to noise ratio (ISNR) has improved.

scholarly journals Optoacoustic model-based inversion using anisotropic adaptive total-variation regularization

2019 ◽  
Vol 16 ◽  
pp. 100142
Author(s):  
Shai Biton ◽  
Nadav Arbel ◽  
Gilad Drozdov ◽  
Guy Gilboa ◽  
Amir Rosenthal
Keyword(s):  
Total Variation ◽  
Total Variation Regularization ◽  
Model Based ◽  
Adaptive Total Variation

scholarly journals An adaptive algorithm for restoring image corrupted by mixed noise

2019 ◽  
pp. 73-82
Author(s):  
Pham Cong Thang ◽  
Tran Thi Thu Thao ◽  
Phan Tran Dang Khoa ◽  
Dinh Viet Sang ◽  
Pham Minh Tuan ◽  
...  
Keyword(s):  
Optimization Problem ◽  
Digital Images ◽  
State Of The Art ◽  
Natural Images ◽  
Total Variation Regularization ◽  
Edge Preservation ◽  
Spatially Adaptive ◽  
Alternating Direction ◽  
Mixed Noise ◽  
Adaptive Total Variation

Image denoising is one of the fundamental problems in image processing. Digital images are often contaminated by noise due to the image acquisition process under poor conditions. In this paper, we propose an effective approach to remove mixed Poisson-Gaussian noise in digital images. Particularly, we propose to use a spatially adaptive total variation regularization term in order to enhance the ability of edge preservation. We also propose an instance of the alternating direction algorithm to solve the proposed denoising model as an optimization problem. The experiments on popular natural images demonstrate that our approach achieves superior accuracy than other recent state-of-the-art techniques.

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