Blur kernel re-initialization for blind image deblurring

2016 ◽  
Author(s):  
Hyukzae Lee ◽  
Changick Kim
Keyword(s):  
Image Deblurring ◽  
Blind Image Deblurring ◽  
Blur Kernel ◽  
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

Spatial-scale-regularized blur kernel estimation for blind image deblurring

2018 ◽  
Vol 68 ◽  
pp. 138-154 ◽  
Author(s):  
Shu Tang ◽  
Xianzhong Xie ◽  
Ming Xia ◽  
Lei Luo ◽  
Peisong Liu ◽  
...  
Keyword(s):  
Spatial Scale ◽  
Kernel Estimation ◽  
Image Deblurring ◽  
Blind Image Deblurring ◽  
Blur Kernel Estimation ◽  
Blur Kernel ◽  
Blind Image

A blind image deblurring algorithm based on relative gradient and sparse representation

2018 ◽  
Vol 32 (34n36) ◽  
pp. 1840087 ◽  
Author(s):  
Qiwei Chen ◽  
Yiming Wang
Keyword(s):  
Image Reconstruction ◽  
Image Restoration ◽  
Sparse Representation ◽  
Kernel Estimation ◽  
Image Deblurring ◽  
Blind Image Deblurring ◽  
Overcomplete Dictionary ◽  
Blur Kernel ◽  
Blind Image

A blind image deblurring algorithm based on relative gradient and sparse representation is proposed in this paper. The layered method restores the image by three steps: edge extraction, blur kernel estimation and image reconstruction. The positive and negative gradients in texture part have reversal changes, and the edge part that reflects the image structure has only one gradient change. According to the characteristic, the edge of the image is extracted by using the relative gradient of image, so as to estimate the blur kernel of the image. In the stage of image reconstruction, in order to overcome the problem of oversize of the image and the overcomplete dictionary matrix, the image is divided into small blocks. An overcomplete dictionary is used for sparse representation, and the image is reconstructed by the iterative threshold shrinkage method to improve the quality of image restoration. Experimental results show that the proposed method can effectively improve the quality of image restoration.

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.

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