Bayesian sparse representation for hyperspectral image super resolution

By Naveed Akhtar, Faisal Shafait, Ajmal Saeed Mian. Computer Vision and Pattern Recognition, 2015

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Hyperspectral and Multispectral Data Fusion: A comparative review of the recent literature (2017, background): “The problem formulation is based on information of the prior distribution in the observed scene such as Gaussian or sparsity promoted Gaussian [38], [57], [58].”
Hyperspectral Image Super-Resolution via Non-local Sparse Tensor Factorization (2017, methodology): “By imposing priors on the distribution of the image intensities, Bayesian approaches [33, 3] apply MAP inference to regularize the fusion problem.”
Hyperspectral Image Super-Resolution via Non-Negative Structured Sparse Representation (2016, background): “Sparsity prior has been shown effective for solving various ill-posed inverse problems under the context of hyperspectral image reconstruction [20], [25], [26], [41], [45].”
Hyperspectral Super-Resolution by Coupled Spectral Unmixing (2015, methodology): “Similarly, [2] constrains neither the basis nor the coefficients to be ≥ 0, and produces a few pixels with negative intensities in most images.”