Self-supervised Learning with Spectral Low-Rank Prior for Hyperspectral Image Reconstruction

Hyperspectral image (HSI) reconstruction from coded measurement is significant for acquiring images with higher spectral resolution than traditional RGB images. Current advanced neural networks have already shown impressive performance in some datasets like CAVE and KAIST. However, these networks rely on a large amount of simulated ground truth, measurement pairs. Unfortunately, in some scenarios, it is hard to obtain a sufficient high-quality HSI training set, resulting in low generalization ability. Although iterative algorithms show good generalization ability, they are limited by slow speed and low reconstruction quality. To address this challenge, in this paper, we propose a self-supervised learning framework, which can train and fine-tune networks using measurements without ground truth. Besides, we propose the spectral low-rank loss function that enables networks to learn the signal model of HSI. Finally, we train and fine-tune a representative deep unfolding network, GAP-net, using our proposed framework. Extensive simulation and real data results show that the proposed self-supervised framework is capable of achieving results competitive with those of supervised networks. Code is available at https://github.com/zjhe02/CASSI-SSL.