Blind image deconvolution with spatially adaptive total variation regularization: erratum

2014 ◽  
Vol 39 (6) ◽  
pp. 1353
Author(s):  
Luxin Yan ◽  
Houzhang Fang ◽  
Sheng Zhong
Keyword(s):  
Total Variation ◽  
Total Variation Regularization ◽  
Image Deconvolution ◽  
Spatially Adaptive ◽  
Blind Image Deconvolution ◽  
Blind Image ◽  
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.

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