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Open AccessFeature PaperArticle

Double-Constraint Inpainting Model of a Single-Depth Image

by Wu Jin 1, Li Zun 1,2,* and Liu Yong 1
1
School of Information Science and Engineering, Wuhan University of Science and Technology, Wuhan 430081, China
2
School of Physics and Electronic Engineering, Xinxiang College, Xinxiang 453000, China
*
Author to whom correspondence should be addressed.
Sensors 2020, 20(6), 1797; https://doi.org/10.3390/s20061797
Received: 14 February 2020 / Revised: 20 March 2020 / Accepted: 20 March 2020 / Published: 24 March 2020
(This article belongs to the Special Issue Data, Signal and Image Processing and Applications in Sensors)
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. View Full-Text
Keywords: depth image inpainting; variable splitting technique; low-rank constraint; nonlocal self-similarity constraint depth image inpainting; variable splitting technique; low-rank constraint; nonlocal self-similarity constraint
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Jin, W.; Zun, L.; Yong, L. Double-Constraint Inpainting Model of a Single-Depth Image. Sensors 2020, 20, 1797.

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