期刊
IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE
卷 43, 期 6, 页码 2133-2140出版社
IEEE COMPUTER SOC
DOI: 10.1109/TPAMI.2020.3017672
关键词
Tensor singular value decomposition; robust principal component analysis; multidimensional image denoising
资金
- National Natural Science Foundation of China [61773302]
- Natural Science Basic Research Plan in Shaanxi Province [2020JZ-19]
The study proposes an enhanced TRPCA algorithm, which explicitly considers the significant difference information between singular values of tensor data, by the weighted tensor Schatten p-norm minimization. Extensive experimental results demonstrate the superiority of the proposed method ETRPCA over several state-of-the-art variant RPCA methods in terms of performance.
Despite the promising results, tensor robust principal component analysis (TRPCA), which aims to recover underlying low-rank structure of clean tensor data corrupted with noise/outliers by shrinking all singular values equally, cannot well preserve the salient content of image. The major reason is that, in real applications, there is a salient difference information between all singular values of a tensor image, and the larger singular values are generally associated with some salient parts in the image. Thus, the singular values should be treated differently. Inspired by this observation, we investigate whether there is a better alternative solution when using tensor rank minimization. In this paper, we develop an enhanced TRPCA (ETRPCA) which explicitly considers the salient difference information between singular values of tensor data by the weighted tensor Schatten p-norm minimization, and then propose an efficient algorithm, which has a good convergence, to solve ETRPCA. Extensive experimental results reveal that the proposed method ETRPCA is superior to several state-of-the-art variant RPCA methods in terms of performance.
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