Lightweight Multiresolution Feature Fusion Network for Spectral Super-Resolution

Abstract

Spectral super-resolution (SR), which reconstructs high spatial-resolution hyperspectral images (HSIs) from RGB inputs, has been demonstrated to be one of the effective computational imaging techniques to acquire HSIs. Though deep neural networks have shown their superiority in such a complex mapping problem, existing networks generally involve a very complex structure with huge amounts of parameters, resulting in giant memory occupation. In this article, a lightweight multiresolution feature fusion network (MRFN) is proposed, which adopts a multiresolution feature extraction and fusion framework to fully explore RGB inputs in different scales of resolution. Specifically, a lightweight feature extraction module (LFEM), which adopts cheap convolution and attention mechanisms, is constructed to explore different scales of features under a lightweight structure. Moreover, a hybrid loss function is proposed by encountering not only pixel-value level reconstruction error but also spectral continuity and fidelity. Experiments over three benchmark datasets, i.e., CAVE, Interdisciplinary Computational Vision Laboratory (ICVL), and NTIRE2022 datasets, have demonstrated that the proposed MRFN can reconstruct HSIs from RGB inputs in higher quality with fewer parameters and computational floating-point operations (FLOPs) compared with several state-of-the-art networks.