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• W3022131966 abstract "Hyperspectral image (HSI) super-resolution refers to enhancing the spatial resolution of a 3-D image with many spectral bands (slices). It is a seriously ill-posed problem when the low-resolution (LR) HSI is the only input. It is better solved by fusing the LR HSI with a high-resolution (HR) multispectral image (MSI) for a 3-D image with both high spectral and spatial resolution. In this article, we propose a novel nonnegative and nonlocal 4-D tensor dictionary learning-based HSI super-resolution model using group-block sparsity. By grouping similar 3-D image cubes into clusters and then conduct super-resolution cluster by cluster using 4-D tensor structure, we not only preserve the structure but also achieve sparsity within the cluster due to the collection of similar cubes. We use 4-D tensor Tucker decomposition and impose nonnegative constraints on the dictionaries and group-block sparsity. Numerous experiments demonstrate that the proposed model outperforms many state-of-the-art HSI super-resolution methods." @default.
• W3022131966 created "2020-05-13" @default.
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• W3022131966 date "2020-12-01" @default.
• W3022131966 modified "2023-10-02" @default.
• W3022131966 title "Nonnegative and Nonlocal Sparse Tensor Factorization-Based Hyperspectral Image Super-Resolution" @default.
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• W3022131966 doi "https://doi.org/10.1109/tgrs.2020.2987530" @default.
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