Edge Preserving Image Restoration

1992 ◽  
pp. 1-13 ◽  
Author(s):  
Mona Abdalla ◽  
Jim Kay
Keyword(s):  
Image Restoration ◽  
Edge Preserving

Fast Fully Data-Driven Image Restoration by means of Edge-Preserving Regularization

2001 ◽  
Vol 7 (1) ◽  
pp. 3-19 ◽  
Author(s):  
Luigi Bedini ◽  
Anna Tonazzini
Keyword(s):  
Image Restoration ◽  
Data Driven ◽  
Edge Preserving

Fast edge-preserving image restoration based on phase information

2007 ◽  
Vol 46 (2) ◽  
pp. 027003
Author(s):  
Tian-xu Zhang
Keyword(s):  
Image Restoration ◽  
Phase Information ◽  
Edge Preserving

Sigmoidal approximations for self-interacting line processes in edge-preserving image restoration

1995 ◽  
Vol 16 (10) ◽  
pp. 1011-1022 ◽  
Author(s):  
L. Bedini ◽  
I. Gerace ◽  
A. Tonazzini
Keyword(s):  
Image Restoration ◽  
Edge Preserving ◽  
Line Processes

scholarly journals Bayesian Image Restoration Using a Large-Scale Total Patch Variation Prior

2011 ◽  
Vol 2011 ◽  
pp. 1-15 ◽  
Author(s):  
Yang Chen ◽  
Weimin Yu ◽  
Yinsheng Li ◽  
Zhou Yang ◽  
Limin Luo ◽  
...  
Keyword(s):  
Image Restoration ◽  
Large Scale ◽  
Computational Cost ◽  
Real Data ◽  
Inverse Image ◽  
Edge Preserving ◽  
Device Architecture ◽  
Mixture Prior ◽  
Ill Posed ◽  
Improved Performance

Edge-preserving Bayesian restorations using nonquadratic priors are often inefficient in restoring continuous variations and tend to produce block artifacts around edges in ill-posed inverse image restorations. To overcome this, we have proposed a spatial adaptive (SA) prior with improved performance. However, this SA prior restoration suffers from high computational cost and the unguaranteed convergence problem. Concerning these issues, this paper proposes a Large-scale Total Patch Variation (LS-TPV) Prior model for Bayesian image restoration. In this model, the prior for each pixel is defined as a singleton conditional probability, which is in a mixture prior form of one patch similarity prior and one weight entropy prior. A joint MAP estimation is thus built to ensure the iteration monotonicity. The intensive calculation of patch distances is greatly alleviated by the parallelization of Compute Unified Device Architecture(CUDA). Experiments with both simulated and real data validate the good performance of the proposed restoration.

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