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