Prinet: A Prior Driven Spectral Super-Resolution Network

Abstract

Spectral super-resolution aims to reconstruct hyperspectral images from RGB images directly. In recent years, convolutional networks have been successfully employed to this task. However, few of them take into account the specific properties of hyperspectral images. In this paper, we attempt to design a super-resolution network, named PriNET, based on two prior knowledge about hyperspectral images. The first one is spectral correlation. According to this property, we design a decomposition network to reconstruct hyperspectral images. In this network, the whole spectral bands of hyperspectral images are divided into several groups, and multiple residual networks are proposed to reconstruct them separately. The second knowledge is that the hyperspectral image should be able to generate its corresponding RGB image. Inspired from it, we design a self-supervised network to fine-tune the reconstruction results of the decomposition network. Finally, these two networks are combined together to constitute PriNET. Experimental results on two hyperspectral datasets demonstrate that the proposed PriNET can achieve better performance than several state-of-the-art networks.