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- W4387448003 abstract "Recently, tensor singular value decomposition (t-SVD) has demonstrated excellent performance in various high-dimensional information processing applications. However, in adapting t-SVD to handle the typical tensor data restoration tasks, such as hyperspectral image (HSI) denoising, the following questions remain inadequately addressed: 1) The existing tensor nuclear norm minimization (TNN) regime treats all tensor singular values alike; thus, it lacks flexibility and dominance in dealing with the sophisticated HSI tensor. 2) The existing t-SVD-based denoising methods can not directly process order- <italic xmlns:mml=http://www.w3.org/1998/Math/MathML xmlns:xlink=http://www.w3.org/1999/xlink>p</i> ( <italic xmlns:mml=http://www.w3.org/1998/Math/MathML xmlns:xlink=http://www.w3.org/1999/xlink>p</i> > 3) tensors; thus, they fail to comprehensively exploit the high-dimensional structural correlation of the HSI tensor along different modes. To address the above challenges, in this study, we first generalize a novel weighted order- <italic xmlns:mml=http://www.w3.org/1998/Math/MathML xmlns:xlink=http://www.w3.org/1999/xlink>p</i> TNN minimization regime, which integrates the adaptively reweighting strategy for matrix, third-order, and order- <italic xmlns:mml=http://www.w3.org/1998/Math/MathML xmlns:xlink=http://www.w3.org/1999/xlink>p</i> tensors in a unified architecture. Subsequently, an efficient subspace low-rank learning model is established, using HSI denoising tasks as an application example to corroborate the superiority of the proposed regime in approximating the high-dimensional low-rank structure of natural tensor data. Extensive experimental results substantiate that our effort surpasses existing state-of-the-art low-rank tensor recovery methods in both restoration accuracy and efficiency. The source code is available at https://github.com/CX-He/WTNN.git." @default.
- W4387448003 created "2023-10-10" @default.
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- W4387448003 date "2023-01-01" @default.
- W4387448003 modified "2023-10-15" @default.
- W4387448003 title "Weighted Order-<i>p</i> Tensor Nuclear Norm Minimization and Its Application to Hyperspectral Image Mixed Denoising" @default.
- W4387448003 doi "https://doi.org/10.1109/lgrs.2023.3322946" @default.
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