Nonblind Image Deblurring Based on Bi-Composition Decomposition by Local Smoothness and Nonlocal Self-Similarity Priors

IEEE Access

Song Gao¹  Chen Ling²  Liping Sun²  He Ren²  Qiaohong Liu² 

[1] College of Agricultural Equipment Engineering, Henan University of Science and Technology, Luoyang, China;School of Medical Instruments, Shanghai University of Medicine and Health Sciences, Shanghai, China;

关键词: Nonblind image deblurring;    bi-component decomposition;    local smoothness;    nonlocal self-similarity;    split Bregman iteration;    four-directional fast gradient projection algorithm;   

DOI  :  10.1109/ACCESS.2019.2915314

来源: DOAJ

【 摘 要 】

Image deblurring is a classical inverse problem in image processing and computer vision. The vital task is to construct the proper image prior model to obtain the high-quality restored image with salient edges and rich details. A new nonblind image deblurring method by combining local smoothness and nonlocal self-similarity of natural images in the regularization framework is proposed. First, the observed image is decomposed into two components: structure component and detail component by a global gradient extraction scheme. Second, the four-directional anisotropic total variation regularization satisfying the local smoothness property is adopted for the structure component, and a new nonlocal statistical modeling for self-similarity is used for the detail component, respectively. At last, the split Bregman-based iteration algorithm and four-directional fast gradient projection algorithm are introduced to optimize the proposed L₁-regularized problem. The extensive experiments demonstrate the efficiency and viability of the proposed method for preserving salient edges and texture details while alleviating the artifacts.

【 授权许可】

Unknown