Single-scale motion blur kernel estimation method based on continuous double frames

2021 ◽  
Author(s):  
Kefei Cheng ◽  
Yan Sun
Keyword(s):  
Estimation Method ◽  
Kernel Estimation ◽  
Motion Blur ◽  
Blur Kernel Estimation ◽  
Single Scale ◽  
Blur Kernel ◽  
Scale Motion

scholarly journals Motion Blur Kernel Estimation via Deep Learning

2018 ◽  
Vol 27 (1) ◽  
pp. 194-205 ◽  
Author(s):  
Xiangyu Xu ◽  
Jinshan Pan ◽  
Yu-Jin Zhang ◽  
Ming-Hsuan Yang
Keyword(s):  
Deep Learning ◽  
Kernel Estimation ◽  
Motion Blur ◽  
Blur Kernel Estimation ◽  
Blur Kernel

scholarly journals Robust Motion Blur Kernel Estimation by Kernel Continuity Prior

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 46162-46175
Author(s):  
Xueling Chen ◽  
Yu Zhu ◽  
Jinqiu Sun ◽  
Yanning Zhang
Keyword(s):  
Kernel Estimation ◽  
Motion Blur ◽  
Blur Kernel Estimation ◽  
Blur Kernel

A self-adaptive learning method for motion blur kernel estimation of the single image

Optik ◽  
2021 ◽  
Vol 248 ◽  
pp. 168023
Author(s):  
Wei Zhou ◽  
Xingxing Hao ◽  
Jin Cui ◽  
Yongxiang Yu ◽  
Xin Cao ◽  
...  
Keyword(s):  
Adaptive Learning ◽  
Kernel Estimation ◽  
Motion Blur ◽  
Learning Method ◽  
Single Image ◽  
Blur Kernel Estimation ◽  
Blur Kernel ◽  
Self Adaptive

scholarly journals Blind Deblurring of Saturated Images Based on Optimization and Deep Learning for Dynamic Visual Inspection on the Assembly Line

Symmetry ◽  
2019 ◽  
Vol 11 (5) ◽  
pp. 678 ◽  
Author(s):  
Bodi Wang ◽  
Guixiong Liu ◽  
Junfang Wu
Keyword(s):  
Deep Learning ◽  
Visual Inspection ◽  
Kernel Estimation ◽  
Visual Quality ◽  
Motion Blur ◽  
Blind Deblurring ◽  
Blur Kernel Estimation ◽  
Wide Range ◽  
Good Score ◽  
Blur Kernel

Image deblurring can improve visual quality and mitigates motion blur for dynamic visual inspection. We propose a method to deblur saturated images for dynamic visual inspection by applying blur kernel estimation and deconvolution modeling. The blur kernel is estimated in a transform domain, whereas the deconvolution model is decoupled into deblurring and denoising stages via variable splitting. Deblurring predicts the mask specifying saturated pixels, which are then discarded, and denoising is learned via the fast and flexible denoising network (FFDNet) convolutional neural network (CNN) at a wide range of noise levels. Hence, the proposed deconvolution model provides the benefits of both model optimization and deep learning. Experiments demonstrate that the proposed method suitably restores visual quality and outperforms existing approaches with good score improvements.

Regularized motion blur-kernel estimation with adaptive sparse image prior learning

2016 ◽  
Vol 51 ◽  
pp. 402-424 ◽  
Author(s):  
Wen-Ze Shao ◽  
Hai-Song Deng ◽  
Qi Ge ◽  
Hai-Bo Li ◽  
Zhi-Hui Wei
Keyword(s):  
Kernel Estimation ◽  
Motion Blur ◽  
Prior Learning ◽  
Image Prior ◽  
Blur Kernel Estimation ◽  
Blur Kernel

scholarly journals Blur Kernel Estimation by Structure Sparse Prior

2020 ◽  
Vol 10 (2) ◽  
pp. 657
Author(s):  
Xiaobin Yuan ◽  
Jingping Zhu ◽  
Xiaobin Li
Keyword(s):  
Sparse Representation ◽  
Estimation Method ◽  
Kernel Estimation ◽  
Blind Image Deblurring ◽  
Locality Constraint ◽  
Blur Kernel Estimation ◽  
Comparable Performance ◽  
Blurred Image ◽  
Blur Kernel ◽  
Sparse Prior

Blind image deblurring tries to recover a sharp version from a blurred image, where blur kernel is usually unknown. Recently, sparse representation has been successfully applied to estimate the blur kernel. However, the sparse representation has not considered the structure relationships among original pixels. In this paper, a blur kernel estimation method is proposed by introducing the locality constraint into sparse representation framework. Both the sparsity regularization and the locality constraint are incorporated to exploit the structure relationships among pixels. The proposed method was evaluated on a real-world benchmark dataset. Experimental results demonstrate that the proposed method achieve comparable performance to the state-of-the-art methods.

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