Double-Constraint Inpainting Model of a Single-Depth Image

Sensors

Li Zun¹  Wu Jin¹  Liu Yong¹ 

[1] School of Information Science and Engineering, Wuhan University of Science and Technology, Wuhan 430081, China;

关键词: depth image inpainting;    variable splitting technique;    low-rank constraint;    nonlocal self-similarity constraint;   

DOI  :  10.3390/s20061797

来源: DOAJ

【 摘 要 】

In real applications, obtained depth images are incomplete; therefore, depth image inpainting is studied here. A novel model that is characterised by both a low-rank structure and nonlocal self-similarity is proposed. As a double constraint, the low-rank structure and nonlocal self-similarity can fully exploit the features of single-depth images to complete the inpainting task. First, according to the characteristics of pixel values, we divide the image into blocks, and similar block groups and three-dimensional arrangements are then formed. Then, the variable splitting technique is applied to effectively divide the inpainting problem into the sub-problems of the low-rank constraint and nonlocal self-similarity constraint. Finally, different strategies are used to solve different sub-problems, resulting in greater reliability. Experiments show that the proposed algorithm attains state-of-the-art performance.

【 授权许可】

Unknown