scholarly journals Depth Image Inpainting: Improving Low Rank Matrix Completion With Low Gradient Regularization

2017 ◽  
Vol 26 (9) ◽  
pp. 4311-4320 ◽  
Author(s):  
Hongyang Xue ◽  
Shengming Zhang ◽  
Deng Cai
Keyword(s):  
Matrix Completion ◽  
Image Inpainting ◽  
Low Rank ◽  
Depth Image ◽  
Rank Matrix ◽  
Low Rank Matrix Completion ◽  
Low Rank Matrix

Non-Local Image Inpainting Using Low-Rank Matrix Completion

2014 ◽  
Vol 34 (6) ◽  
pp. 111-122 ◽  
Author(s):  
Wei Li ◽  
Lei Zhao ◽  
Zhijie Lin ◽  
Duanqing Xu ◽  
Dongming Lu
Keyword(s):  
Matrix Completion ◽  
Image Inpainting ◽  
Low Rank ◽  
Rank Matrix ◽  
Non Local ◽  
Low Rank Matrix Completion ◽  
Low Rank Matrix ◽  
Local Image

scholarly journals A Singular Value Thresholding with Diagonal-Update Algorithm for Low-Rank Matrix Completion

2020 ◽  
Vol 2020 ◽  
pp. 1-14
Author(s):  
Yong-Hong Duan ◽  
Rui-Ping Wen ◽  
Yun Xiao
Keyword(s):  
Matrix Completion ◽  
Arithmetic Operation ◽  
Computational Cost ◽  
Singular Value ◽  
Low Rank ◽  
Simple Arithmetic ◽  
Rank Matrix ◽  
Low Rank Matrix Completion ◽  
Low Rank Matrix ◽  
Singular Value Thresholding

The singular value thresholding (SVT) algorithm plays an important role in the well-known matrix reconstruction problem, and it has many applications in computer vision and recommendation systems. In this paper, an SVT with diagonal-update (D-SVT) algorithm was put forward, which allows the algorithm to make use of simple arithmetic operation and keep the computational cost of each iteration low. The low-rank matrix would be reconstructed well. The convergence of the new algorithm was discussed in detail. Finally, the numerical experiments show the effectiveness of the new algorithm for low-rank matrix completion.

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