Single Hyperspectral Image Super-Resolution with Grouped Deep Recursive Residual Network

Abstract

Fusing a low spatial resolution hyperspectral images (HSIs) with an high spatial resolution conventional (e.g., RGB) image has underpinned much of recent progress in HSIs super-resolution. However, such a scheme requires this pair of images to be well registered, which is often difficult to be complied with in real applications. To address this problem, we present a novel single HSI super-resolution method, termed Grouped Deep Recursive Residual Network (GDRRN), which learns to directly map an input low resolution HSI to a high resolution HSI with a specialized deep neural network. To well depict the complicated non-linear mapping function with a compact network, a grouped recursive module is embedded into the global residual structure to transform the input HSIs. In addition, we conjoin the traditional mean squared error (MSE) loss with the spectral angle mapper (SAM) loss together to learn the network parameters, which enables to reduce both the numerical error and spectral distortion in the super-resolution results, and ultimately improve the performance. Sufficient experiments on the benchmark HSI dataset demonstrate the effectiveness of the proposed method in terms of single HSI super-resolution.