Spectral Superresolution of Hyperspectral Imagery Using Reweighted $\ell _{1}$ Spatial Filtering

Sparsity-based models have enabled significant advances in many image processing tasks. Hyperspectral imagery (HSI) in particular has benefited from these approaches due to the significant low-dimensional structure in both spatial and spectral dimensions. Specifically, previous work has shown that sparsity models can be used for spectral superresolution, where spectral signatures with HSI-level resolution are recovered from measurements with multispectral-level resolution (i.e., an order of magnitude fewer spectral bands). In this letter, we expand on those results by introducing a new inference approach known as reweighted l₁ spatial filtering (RWL1-SF). RWL1-SF incorporates a more sophisticated signal model that allows for variations in the SNR at each pixel as well as spatial dependences between neighboring pixels. The results demonstrate that the proposed approach leverages signal structure beyond simple sparsity to achieve significant improvements in spectral superresolution.