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Open AccessArticle

Blur Kernel Estimation by Structure Sparse Prior

1
The School of Electronic and Information Engineering, Xi’an Jiaotong University, Xi’an 710049, China
2
The Xi’an Institute of Optics and Precision Mechanics, Chinese Academy of Sciences, Xi’an 710119, China
3
The Beijing Institute of Remote Sensing Information, Beijing 100192, China
*
Author to whom correspondence should be addressed.
Appl. Sci. 2020, 10(2), 657; https://doi.org/10.3390/app10020657
Received: 5 December 2019 / Revised: 8 January 2020 / Accepted: 10 January 2020 / Published: 16 January 2020
(This article belongs to the Section Computing and Artificial Intelligence)
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.
Keywords: blind image deblurring; blur kernel estimation, structure information; sparsity regularization; locality constraint blind image deblurring; blur kernel estimation, structure information; sparsity regularization; locality constraint
MDPI and ACS Style

Yuan, X.; Zhu, J.; Li, X. Blur Kernel Estimation by Structure Sparse Prior. Appl. Sci. 2020, 10, 657.

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