Journal
IEEE TRANSACTIONS ON IMAGE PROCESSING
Volume 30, Issue -, Pages 3084-3097Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TIP.2021.3058590
Keywords
Superresolution; Sparse matrices; Spatial resolution; Dictionaries; Correlation; Tensors; Task analysis; Hyperspectral and multispectral images fusion; low-rank representation; structured sparse; subspace low-rank recovery; affinity matrix
Funding
- National Natural Science Foundation of China (NSFC) [61771391]
- Science, Technology, and Innovation Commission of Shenzhen Municipality [JCYJ20170815162956949, JCYJ20180306171146740]
- Innovation Foundation for Doctor Dissertation of Northwestern Polytechnical University [CX201917]
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This paper proposes a novel hyperspectral image super-resolution method that fully considers the spatial/spectral relationships between available HR-MSI and LR-HSI, achieving better performance on three benchmark datasets in terms of both visual and quantitative evaluation compared to state-of-the-art methods.
Hyperspectral image super-resolution by fusing high-resolution multispectral image (HR-MSI) and low-resolution hyperspectral image (LR-HSI) aims at reconstructing high resolution spatial-spectral information of the scene. Existing methods mostly based on spectral unmixing and sparse representation are often developed from a low-level vision task perspective, they cannot sufficiently make use of the spatial and spectral priors available from higher-level analysis. To this issue, this paper proposes a novel HSI super-resolution method that fully considers the spatial/spectral subspace low-rank relationships between available HR-MSI/LR-HSI and latent HSI. Specifically, it relies on a new subspace clustering method named structured sparse low-rank representation (SSLRR), to represent the data samples as linear combinations of the bases in a given dictionary, where the sparse structure is induced by low-rank factorization for the affinity matrix. Then we exploit the proposed SSLRR model to learn the SSLRR along spatial/spectral domain from the MSI/HSI inputs. By using the learned spatial and spectral low-rank structures, we formulate the proposed HSI super-resolution model as a variational optimization problem, which can be readily solved by the ADMM algorithm. Compared with state-of-the-art hyperspectral super-resolution methods, the proposed method shows better performance on three benchmark datasets in terms of both visual and quantitative evaluation.
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