Single Hyperspectral Image Super-Resolution Using Admm-Adam Theory

In the remote sensing field, the spatial resolution of hyperspectral images (HSIs) is poor compared to RGB and multispectral images. Hence, hyperspectral image super-resolution (HISR) has become a popular topic recently. A branch of HISR methods is based on image fusion, but these methods rely on high-spatial-resolution counterpart image (e.g., multispectral image of the same scene) that is, however, not always available. Therefore, developing single hyperspectral image super-resolution (SHISR) method is highly desired. Due to the lack of abundant high-quality HSIs (i.e., big data) in satellite remote sensing, deep learning itself would be insufficient to well solve SHISR. We solve SHISR based on the recently invented ADMM-Adam learning theory, which blends the advantages from deep learning and convex optimization, thereby allowing software engineers to solve various challenging inverse problems without big data and sophisticated regularizer. For the first time, ADMM-Adam is adopted to solve SHISR in this paper, and experimental evidences well support its superiority even just with small data.