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Construction and solution of an adaptive image-restoration model for removing blur and mixed noise

[+] Author Affiliations
Youquan Wang, Lihong Cui

Beijing University of Chemical Technology, Department of Mathematics, 15 Beisanhuan East Road, ChaoYang District, Beijing 100029, China

Yigang Cen

Beijing Jiaotong University, School of Computer and Information Technology, 3 Shangyuancun, HaiDian District, Beijing 100044, China

Jianjun Sun

Beijing University of Chemical Technology, College of Chemical Engineering, 15 Beisanhuan East Road, ChaoYang District, Beijing 100029, China

J. Electron. Imaging. 25(2), 023013 (Mar 28, 2016). doi:10.1117/1.JEI.25.2.023013
History: Received October 20, 2015; Accepted February 23, 2016
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Abstract.  We establish a practical regularized least-squares model with adaptive regularization for dealing with blur and mixed noise in images. This model has some advantages, such as good adaptability for edge restoration and noise suppression due to the application of a priori spatial information obtained from a polluted image. We further focus on finding an important feature of image restoration using an adaptive restoration model with different regularization parameters in polluted images. A more important observation is that the gradient of an image varies regularly from one regularization parameter to another under certain conditions. Then, a modified graduated nonconvexity approach combined with a median filter version of a spatial information indicator is proposed to seek the solution of our adaptive image-restoration model by applying variable splitting and weighted penalty techniques. Numerical experiments show that the method is robust and effective for dealing with various blur and mixed noise levels in images.

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Citation

Youquan Wang ; Lihong Cui ; Yigang Cen and Jianjun Sun
"Construction and solution of an adaptive image-restoration model for removing blur and mixed noise", J. Electron. Imaging. 25(2), 023013 (Mar 28, 2016). ; http://dx.doi.org/10.1117/1.JEI.25.2.023013


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