@article{8719982,
  abstract     = {{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.}},
  author       = {{Xue, Jize and Zhao, Yong-Qiang and Bu, Yuanyang and Liao, Wenzhi and Chan, Jonathan Cheung-Wai and Philips, Wilfried}},
  issn         = {{1057-7149}},
  journal      = {{IEEE TRANSACTIONS ON IMAGE PROCESSING}},
  keywords     = {{Computer Graphics and Computer-Aided Design,Software,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}},
  language     = {{eng}},
  pages        = {{3084--3097}},
  title        = {{Spatial-spectral structured sparse low-rank representation for hyperspectral image super-resolution}},
  url          = {{http://doi.org/10.1109/tip.2021.3058590}},
  volume       = {{30}},
  year         = {{2021}},
}

Altmetric

View in Altmetric

Web of Science

Times cited: