scholarly journals Natural Image Deblurring Based on Ringing Artifacts Removal via Knowledge-Driven Gradient Distribution Priors

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 129975-129991
Author(s):  
Hongtian Zhao ◽  
Hua Yang ◽  
Hang Su ◽  
Shibao Zheng
Keyword(s):  
Image Deblurring ◽  
Natural Image ◽  
Gradient Distribution ◽  
Ringing Artifacts ◽  
Artifacts Removal

Natural image deblurring based on L0-regularization and kernel shape optimization

2018 ◽  
Vol 77 (20) ◽  
pp. 26239-26257 ◽  
Author(s):  
Fengjun Zhang ◽  
Wei Lu ◽  
Hongmei Liu ◽  
Fei Xue
Keyword(s):  
Shape Optimization ◽  
Image Deblurring ◽  
Natural Image

Blind natural image deblurring with edge preservation based on L0-regularized gradient prior

Optik ◽  
2021 ◽  
Vol 225 ◽  
pp. 165735
Author(s):  
Yaozong Zhang ◽  
Yu Shi ◽  
Lei Ma ◽  
Jinmeng Wu ◽  
Lei Wang ◽  
...  
Keyword(s):  
Image Deblurring ◽  
Natural Image ◽  
Edge Preservation

scholarly journals Autoregressive Models and Non-Local Self Similarity in Sparse Representation for Image Deblurring

2019 ◽  
Vol 8 (9) ◽  
pp. 443-449
Keyword(s):  
Local Area ◽  
Image Deblurring ◽  
Input Image ◽  
Autoregressive Models ◽  
Natural Image ◽  
Self Similarity ◽  
High Quality Image ◽  
Image Patches ◽  
Non Local ◽  
Natural Will

Local area within a normal natural image can be thought as a stationary process. This can be modelled well using autoregressive models. In this paper, a set of autoregressive models will be learned from a collection of high quality image patches. Out of these models, one will be selected adaptively and will be used to regularize the input image patches. In addition to the autoregressive models, a non-local self-similarity condition was proposed. The autoregressive models will exploit local correlation of individual image, but a natural will have many repetitive structures. These structures, which are basically redundant, are very much useful in image deblurring. The performance of these schemes is verified by applying to image deblurring

scholarly journals An Improved Adaptive Deconvolution Algorithm for Single Image Deblurring

2014 ◽  
Vol 2014 ◽  
pp. 1-11 ◽  
Author(s):  
Hsin-Che Tsai ◽  
Jiunn-Lin Wu
Keyword(s):  
Motion Blur ◽  
Natural Image ◽  
Image Deconvolution ◽  
Latent Image ◽  
Ringing Artifacts ◽  
Spread Function ◽  
Degraded Image ◽  
Deconvolution Problem ◽  
Sharp Edges ◽  
Image Details

One of the most common defects in digital photography is motion blur caused by camera shake. Shift-invariant motion blur can be modeled as a convolution of the true latent image and a point spread function (PSF) with additive noise. The goal of image deconvolution is to reconstruct a latent image from a degraded image. However, ringing is inevitable artifacts arising in the deconvolution stage. To suppress undesirable artifacts, regularization based methods have been proposed using natural image priors to overcome the ill-posedness of deconvolution problem. When the estimated PSF is erroneous to some extent or the PSF size is large, conventional regularization to reduce ringing would lead to loss of image details. This paper focuses on the nonblind deconvolution by adaptive regularization which preserves image details, while suppressing ringing artifacts. The way is to control the regularization weight adaptively according to the image local characteristics. We adopt elaborated reference maps that indicate the edge strength so that textured and smooth regions can be distinguished. Then we impose an appropriate constraint on the optimization process. The experiments’ results on both synthesized and real images show that our method can restore latent image with much fewer ringing and favors the sharp edges.

An Efficient Natural Image Deblurring Algorithm

2013 ◽  
pp. 576-583 ◽  
Author(s):  
Rajesh R. Pillai ◽  
Vandana Dixit Kaushik ◽  
Phalguni Gupta
Keyword(s):  
Image Deblurring ◽  
Natural Image
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