Blind Multiframe Image Deconvolution Using Anisotropic Spatially Adaptive Filtering for Denoising and Regularization

2007 ◽  
pp. 95-139
Author(s):  
Vladimir Katkovnik ◽  
Jaakko Astola ◽  
Karen Egiazarian
2018 ◽  
Vol 9 (9) ◽  
pp. 4569 ◽  
Author(s):  
Yihan Wang ◽  
Tong Lu ◽  
Jiao Li ◽  
Wenbo Wan ◽  
Wenjuan Ma ◽  
...  

2011 ◽  
Vol 66 (3) ◽  
pp. 337-346 ◽  
Author(s):  
E. Pardo-Iguzquiza ◽  
V.F. Rodríguez-Galiano ◽  
M. Chica-Olmo ◽  
Peter M. Atkinson

2014 ◽  
Vol 2014 ◽  
pp. 1-8 ◽  
Author(s):  
Yaduan Ruan ◽  
Houzhang Fang ◽  
Qimei Chen

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.


2008 ◽  
Author(s):  
Giacomo Boracchi ◽  
Alessandro Foi ◽  
Vladimir Katkovnik ◽  
Karen Egiazarian

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