Spectral Non-gaussianity for Blind Image Deblurring

2011 ◽  
pp. 144-151 ◽  
Author(s):  
Aftab Khan ◽  
Hujun Yin
Keyword(s):  
Image Deblurring ◽  
Blind Image Deblurring ◽  
Blind Image

Sparsity and Smoothing Multi-Regularization Constraints for Blind Image Deblurring

2013 ◽  
Vol 24 (5) ◽  
pp. 1143-1154 ◽  
Author(s):  
Shu TANG ◽  
Wei-Guo GONG ◽  
Jian-Hua ZHONG
Keyword(s):  
Image Deblurring ◽  
Blind Image Deblurring ◽  
Blind Image

Blind image deblurring using group sparse representation

2020 ◽  
Vol 102 ◽  
pp. 102736 ◽  
Author(s):  
Zhenhua Xu ◽  
Huasong Chen ◽  
Zhenhua Li
Keyword(s):  
Sparse Representation ◽  
Image Deblurring ◽  
Blind Image Deblurring ◽  
Blind Image

scholarly journals Blind Image Deblurring Based on Local Edges Selection

2019 ◽  
Vol 9 (16) ◽  
pp. 3274
Author(s):  
Han ◽  
Kan
Keyword(s):  
Image Deblurring ◽  
Color Images ◽  
Clear Image ◽  
Blind Image Deblurring ◽  
Alternating Direction ◽  
Dark Channel ◽  
The Difference ◽  
And Performance ◽  
Image Edges ◽  
Blind Image

The edges of images are less sparse when images become blurred. Selecting effective image edges is a vital step in image deblurring, which can help us to build image deblurring models more accurately. While global edges selection methods tend to fail in capturing dense image structures, the edges are easy to be affected by noise and blur. In this paper, we propose an image deblurring method based on local edges selection. The local edges are selected by the difference between the bright channel and the dark channel. Then a novel image deblurring model including local edges regularization term is established. The obtaining of a clear image and blurring kernel is based on alternating iterations, in which the clear image is obtained by the alternating direction method of multipliers (ADMM). In the experiments, tests are carried out on gray value images, synthetic color images and natural color images. Compared with other state-of-the-art blind image deblurring methods, the visualization results and performance verify the effectiveness of our method.

scholarly journals Blind Image Deblurring using the l0 Gradient Prior

2019 ◽  
Vol 9 ◽  
pp. 124-142 ◽  
Author(s):  
Jérémy Anger ◽  
Gabriele Facciolo ◽  
Mauricio Delbracio
Keyword(s):  
Image Deblurring ◽  
Blind Image Deblurring ◽  
Blind Image

Deep Regressor Networks for Blind Image Deblurring

2021 ◽  
Author(s):  
Rafael G. Pires ◽  
Daniel F. S. Santos ◽  
Leandro A. Passos ◽  
Joao P. Papa
Keyword(s):  
Image Deblurring ◽  
Blind Image Deblurring ◽  
Blind Image

scholarly journals BID: An Effective Blind Image Deblurring Scheme to Estimate the Blur Kernel for Various Scenarios

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 9185-9195
Author(s):  
Hong Zhang ◽  
Yawei Li ◽  
Yujie Wu ◽  
Zeyu Zhang
Keyword(s):  
Image Deblurring ◽  
Blind Image Deblurring ◽  
Blur Kernel ◽  
Blind Image
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