Image deblurring and denoising with non-local regularization constraint

Author(s):  
Peter van Beek ◽  
Junlan Yang ◽  
Shuhei Yamamoto ◽  
Yasuhiro Ueda
2021 ◽  
Vol 397 ◽  
pp. 125977
Author(s):  
Jingjing Liu ◽  
Ruijie Ma ◽  
Xiaoyang Zeng ◽  
Wanquan Liu ◽  
Mingyu Wang ◽  
...  

CALCOLO ◽  
2008 ◽  
Vol 45 (3) ◽  
pp. 149-175 ◽  
Author(s):  
Antonio Aricò ◽  
Marco Donatelli ◽  
Stefano Serra-Capizzano

2011 ◽  
Vol 5 (2) ◽  
pp. 511-530 ◽  
Author(s):  
Gabriel Peyré ◽  
◽  
Sébastien Bougleux ◽  
Laurent Cohen ◽  

2013 ◽  
Vol 411-414 ◽  
pp. 1164-1169 ◽  
Author(s):  
Zhi Ming Wang ◽  
Hong Bao

Image deblurring with noise is a typical ill-posed problem needs regularization. Various regularization models were proposed during several decades study, such as Tikhonov and TV. A new regularization model based non-local similarity constrains is proposed in this paper, which used l2 non-local norms and could be easily solved by fast non-local image denoising algorithm. By combining with Bregmanrized operator splitting (BOS) algorithm, a fast and efficient iterative three step image deblurring scheme is given. Experimental results show that proposed regularization model obtained better results on ten common test images than other similar regularization model including newly proposed NLTV regularization, both in deblurring performance (PSNR and MSSIM) and processing speed.


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