期刊
IEEE TRANSACTIONS ON IMAGE PROCESSING
卷 31, 期 -, 页码 3793-3808出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TIP.2022.3176220
关键词
Tensors; Transforms; Imaging; TV; Neural networks; Discrete Fourier transforms; Nonhomogeneous media; Self-supervised learning; nonlinear transform; tensor nuclear norm; multi-dimensional image
资金
- National Natural Science Foundation of China (NSFC) [61876203, 12171072, 12001446, 62101294]
- Applied Basic Research Project of Sichuan Province [2021YJ0107]
- Key Project of Applied Basic Research in Sichuan Province [2020YJ0216]
- National Key Research and Development Program of China [2020YFA0714001]
- Fundamental Research Funds for the Central Universities [JBK2202049, JBK2102001]
- Hong Kong Research Grant Council (HKRGC) through the General Research Fund (GRF) [12300218, 12300519, 17201020, 17300021, C1013-21GF, C7004-21GF]
- Joint NSFC-RGC [N-HKU76921]
This paper proposes a nonlinear multilayer neural network to learn a nonlinear transform using the observed tensor in a self-supervised manner. Experimental results demonstrate the superior performance of the proposed method across different data and tasks.
Recently, transform-based tensor nuclear norm (TNN) minimization methods have received increasing attention for recovering third-order tensors in multi-dimensional imaging problems. The main idea of these methods is to perform the linear transform along the third mode of third-order tensors and then minimize the nuclear norm of frontal slices of the transformed tensor. The main aim of this paper is to propose a nonlinear multilayer neural network to learn a nonlinear transform by solely using the observed tensor in a self-supervised manner. The proposed network makes use of the low-rank representation of the transformed tensor and data-fitting between the observed tensor and the reconstructed tensor to learn the nonlinear transform. Extensive experimental results on different data and different tasks including tensor completion, background subtraction, robust tensor completion, and snapshot compressive imaging demonstrate the superior performance of the proposed method over state-of-the-art methods.
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