Hyperspectral Reconstruction From RGB Images via Physically Guided Graph Deep Prior Learning

Recovering the latent hyperspectral image (HSI) from RGB or multispectral image (MSI), which is dubbed spectral super-resolution (SSR), has demonstrated outstanding performance owing to the advancements in convolutional neural networks (CNNs). However, most of the current algorithms concentrate on the pursuit of networks with more expensive or complex structures, while ignoring the significant role of physical degradation models in SSR. In addition, the inherent defects of CNN make these networks focus more on the local correlation, while their ability to model the long-range correlations in the spectral and spatial domains still has room to improve. To overcome this shortcoming, we propose a physical degradation-guided deep prior learning network (PGDL-Net) for SSR via unfolding the optimization process of the blind SSR model, in which the priors of unknown spectral response function (SRF) and latent HSI are learned explicitly and represented by proximal operators. To jointly extract the local and non-local information, we design a hybrid graph Transformer as the proximal operator to solve the latent HSI. Furthermore, to ensure efficient learning of SRF and HSI, we also propose a novel loss function constraining the reconstruction error, degradation consistency, and observation fidelity for the learned SRF and HSI. Experimental results on multiple datasets illustrate the improved performance and stability of our method in SSR.