Sharpening Hyperspectral Images Using Spatial and Spectral Priors in a Plug-and-Play Algorithm

摘要

This paper proposes using both spatial and spectral regular-izers/priors for hyperspectral image sharpening. Leveraging the recent plug-and-play framework, we plug two Gaussian-mixture-based denois-ers into the iterations of an alternating direction method of multipliers (ADMM): a spatial regularizer learned from the observed multispectral image, and a spectral regularizer trained using the hyperspectral data. The proposed approach achieves very competitive results, improving the performance over using a single regularizer. Furthermore, the spectral regularizer can be used to classify the image pixels, opening the door to class-adapted models.