Super-resolution Sharpening-Demosaicking with Spatially Adaptive Total-Variation Image Regularization

2005 ◽  
pp. 246-256 ◽  
Author(s):  
Takahiro Saito ◽  
Takashi Komatsu
Keyword(s):  
Total Variation ◽  
Super Resolution ◽  
Spatially Adaptive ◽  
Image Regularization ◽  
Adaptive Total Variation

scholarly journals Spatially Adaptive Tensor Total Variation-Tikhonov Model for Depth Image Super Resolution

IEEE Access ◽  
2017 ◽  
Vol 5 ◽  
pp. 13857-13867 ◽  
Author(s):  
Gang Zhong ◽  
Sen Xiang ◽  
Peng Zhou ◽  
Li Yu
Keyword(s):  
Total Variation ◽  
Super Resolution ◽  
Depth Image ◽  
Spatially Adaptive ◽  
Image Super Resolution

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.

Spatially adaptive total variation deblurring with split Bregman technique

2018 ◽  
Vol 12 (6) ◽  
pp. 948-958 ◽  
Author(s):  
Mahdi Dodangeh ◽  
Isabel N. Figueiredo ◽  
Gil Gonçalves
Keyword(s):  
Total Variation ◽  
Split Bregman ◽  
Spatially Adaptive ◽  
Adaptive Total Variation
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