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 from a research team led by Prof. YUAN Xiaobin  from Xi'an Institute of Optics and Precision Mechanics (XIOPM) of the Chinese Academy of Sciences (CAS) 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.

 
Diagram of the proposed blur kernel estimation method. (Image by XIOPM)

(Original research article “Applied Sciences” (2020)  https://doi.org/10.3390/app10020657 )