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 efficient algorithm for adaptive total variation based image decomposition and restoration

2014 ◽  
Vol 24 (2) ◽  
pp. 405-415 ◽  
Author(s):  
Xinwu Liu ◽  
Lihong Huang
Keyword(s):  
Total Variation ◽  
Image Decomposition ◽  
Energy Functional ◽  
Total Variation Regularization ◽  
Split Bregman Method ◽  
Split Bregman ◽  
Edge Preserving ◽  
Sharp Edges ◽  
Adaptive Total Variation ◽  
Important Structure

Abstract With the aim to better preserve sharp edges and important structure features in the recovered image, this article researches an improved adaptive total variation regularization and H−1 norm fidelity based strategy for image decomposition and restoration. Computationally, for minimizing the proposed energy functional, we investigate an efficient numerical algorithm—the split Bregman method, and briefly prove its convergence. In addition, comparisons are also made with the classical OSV (Osher–Sole–Vese) model (Osher et al., 2003) and the TV-Gabor model (Aujol et al., 2006), in terms of the edge-preserving capability and the recovered results. Numerical experiments markedly demonstrate that our novel scheme yields significantly better outcomes in image decomposition and denoising than the existing models.

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.

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