scholarly journals Multi-Regularization-Constrained Blur Kernel Estimation Method for Blind Motion Deblurring

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 5296-5311 ◽  
Author(s):  
Shu Tang ◽  
Wanpeng Zheng ◽  
Xianzhong Xie ◽  
Tao He ◽  
Peng Yang ◽  
...  
Keyword(s):  
Estimation Method ◽  
Kernel Estimation ◽  
Motion Deblurring ◽  
Blur Kernel Estimation ◽  
Blur Kernel ◽  
Blind Motion Deblurring

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.

scholarly journals Research on Blind Super-Resolution Technology for Infrared Images of Power Equipment Based on Compressed Sensing Theory

Sensors ◽  
2021 ◽  
Vol 21 (12) ◽  
pp. 4109
Author(s):  
Yan Wang ◽  
Lingjie Wang ◽  
Bingcong Liu ◽  
Hongshan Zhao
Keyword(s):  
Compressed Sensing ◽  
Estimation Method ◽  
Kernel Estimation ◽  
Super Resolution ◽  
Reconstruction Algorithm ◽  
Power Equipment ◽  
Infrared Images ◽  
Blur Kernel Estimation ◽  
Resolution Algorithm ◽  
Blur Kernel

Infrared images of power equipment play an important role in power equipment status monitoring and fault identification. Aiming to resolve the problems of low resolution and insufficient clarity in the application of infrared images, we propose a blind super-resolution algorithm based on the theory of compressed sensing. It includes an improved blur kernel estimation method combined with compressed sensing theory and an improved infrared image super-resolution reconstruction algorithm based on block compressed sensing theory. In the blur kernel estimation method, we propose a blur kernel estimation algorithm under the compressed sensing framework to realize the estimation of the blur kernel from low-resolution images. In the estimation process, we define a new norm to constrain the gradient image in the iterative process by analyzing the significant edge intensity changes before and after the image is blurred. With the norm, the salient edges can be selected and enhanced, the intermediate latent image generated by the iteration can move closer to the clear image, and the accuracy of the blur kernel estimation can be improved. For the super-resolution reconstruction algorithm, we introduce a blur matrix and a regular total variation term into the traditional compressed sensing model and design a two-step total variation sparse iteration (TwTVSI) algorithm. Therefore, while ensuring the computational efficiency, the boundary effect caused by the block processing inside the image is removed. In addition, the design of the TwTVSI algorithm can effectively process the super-resolution model of compressed sensing with a sparse dictionary, thereby breaking through the reconstruction performance limitation of the traditional regularized super-resolution method of compressed sensing due to the lack of sparseness in the signal transform domain. The final experimental results also verify the effectiveness of our blind super-resolution algorithm.

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