Deepcasd: An End-to-End Approach for Multi-Spectral Image Super-Resolution

Abstract

Multi-spectral (MS) image super-resolution aims to reconstruct super-resolved multi-channel images from their low-resolution images by regularizing the image to be reconstructed. Recently data-driven regularization techniques based on sparse modeling and deep learning have achieved substantial improvements in single image reconstruction problems. Inspired by these data-driven methods, we develop a novel coupled analysis and synthesis dictionary (CASD) model for MS image super-resolution, by exploiting a regularizer that operates within, as well as across, multiple spectral channels using convolutional dictionaries. To learn the CASD model parameters, we propose a deep dictionary learning framework, named DeepCASD, by unfolding and training an end-to-end CASD based reconstruction network over an image data set. Experimental results show that the DeepCASD framework exhibits improved performance on multi-spectral image super-resolution compared to state-of-the-art learning based super-resolution algorithms.