Implementation of an Image Restoration with Block Iteration Method for Spatially Variant Blur Models

2016 ◽  
pp. 414-420
Author(s):  
Ji Yeon Lee ◽  
Kuk Won Ko ◽  
Sangjoon Lee
Keyword(s):  
Image Restoration ◽  
Iteration Method ◽  
Block Iteration ◽  
Spatially Variant

Nonlocal patch-based method on spatially-variant amoeba morphology for image restoration

Optik ◽  
2015 ◽  
Vol 126 (2) ◽  
pp. 283-288
Author(s):  
Shuo Yang ◽  
Jian-Xun Li
Keyword(s):  
Image Restoration ◽  
Spatially Variant

Bounds for the Condition Numbers of Spatially-variant Convolution Matrices in Image Restoration Problems

2011 ◽  
Author(s):  
Stanley H. Chan ◽  
Ankit K. Jain ◽  
Truong Q. Nguyen ◽  
Edmund Y. Lam
Keyword(s):  
Image Restoration ◽  
Condition Numbers ◽  
Spatially Variant

scholarly journals A Multilevel Iteration Method for Solving a Coupled Integral Equation Model in Image Restoration

Mathematics ◽  
2020 ◽  
Vol 8 (3) ◽  
pp. 346
Author(s):  
Hongqi Yang ◽  
Bing Zhou
Keyword(s):  
Integral Equation ◽  
Image Restoration ◽  
Collocation Method ◽  
Iteration Method ◽  
Equation Model ◽  
Integral Approximation ◽  
Fully Discrete ◽  
Computation Efficiency ◽  
The Matrix ◽  
Parameter Choice Strategy

The problem of out-of-focus image restoration can be modeled as an ill-posed integral equation, which can be regularized as a second kind of equation using the Tikhonov method. The multiscale collocation method with the compression strategy has already been developed to discretize this well-posed equation. However, the integral computation and solution of the large multiscale collocation integral equation are two time-consuming processes. To overcome these difficulties, we propose a fully discrete multiscale collocation method using an integral approximation strategy to compute the integral, which efficiently converts the integral operation to the matrix operation and reduces costs. In addition, we also propose a multilevel iteration method (MIM) to solve the fully discrete integral equation obtained from the integral approximation strategy. Herein, the stopping criterion and the computation complexity that correspond to the MIM are shown. Furthermore, a posteriori parameter choice strategy is developed for this method, and the final convergence order is evaluated. We present three numerical experiments to display the performance and computation efficiency of our proposed methods.

Parallel image restoration with spatially variant point spread function: description and first clinical results

2001 ◽  
Author(s):  
Andrew Shearer ◽  
Gerard Gorman ◽  
Triona O'Doherty ◽  
Wilhelm J. van der Putten ◽  
Peter McCarthy ◽  
...  
Keyword(s):  
Image Restoration ◽  
Point Spread Function ◽  
Clinical Results ◽  
Point Spread ◽  
Parallel Image ◽  
Spread Function ◽  
Spatially Variant

Image restoration using spatially variant hyper-Laplacian prior

2018 ◽  
Vol 13 (1) ◽  
pp. 155-162 ◽  
Author(s):  
Junting Cheng ◽  
Yi Gao ◽  
Boyang Guo ◽  
Wangmeng Zuo
Keyword(s):  
Image Restoration ◽  
Spatially Variant

scholarly journals On the GSOR iteration method for image restoration

2019 ◽  
Vol 0 (0) ◽  
pp. 0-0
Author(s):  
Mehdi Bastani ◽  
◽  
Davod Khojasteh Salkuyeh ◽  
Keyword(s):  
Image Restoration ◽  
Iteration Method
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