Hyperspectral Image Super-Resolution via Non-Negative Structured Sparse Representation

By Weisheng Dong, Fazuo Fu, Guangming Shi, Xun Cao, Jinjian Wu, Guangyu Li, Xin Li. IEEE Transactions on Image Processing, 2016

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Edge-Enhanced GAN for Remote Sensing Image Superresolution (2019, background): “of HR remote sensing images have been proposed [37]–[39].”
Learning a Low Tensor-Train Rank Representation for Hyperspectral Image Super-Resolution (2019, methodology): “We compare the LTTR method with three current state-of-the-art HSI super-resolution approaches, including non-negative structured sparse representation (NSSR) [33], coupled spectral unmixing (CSU) [28], and nonlocal sparse tensor factorization method (NLSTF) [35].”
Deep Hyperspectral Image Sharpening (2018, unknown): “sparse representation (NSSR) [1], and NLSTF method [42].”
Fusing Hyperspectral and Multispectral Images via Coupled Sparse Tensor Factorization (2018, methodology): “The work [27] proposes a nonnegative dictionary-learning algo-rithm to learn the spectral basis and uses a structured sparse coding approach to estimate the coefﬁcient matrix.”