Model Inspired Autoencoder for Unsupervised Hyperspectral Image Super-Resolution

This article focuses on hyperspectral image (HSI) super-resolution that aims to fuse a low-spatial-resolution HSI and a high-spatial-resolution multispectral image to form a high-spatial-resolution HSI (HR-HSI). Existing deep learning-based approaches are mostly supervised that rely on a large number of labeled training samples, which is unrealistic. The commonly used model-based approaches are unsupervised and flexible but rely on handcrafted priors. Inspired by the specific properties of model, we make the first attempt to design a model-inspired deep network for HSI super-resolution in an unsupervised manner. This approach consists of an implicit autoencoder network built on the target HR-HSI that treats each pixel as an individual sample. The nonnegative matrix factorization (NMF) of the target HR-HSI is integrated into the autoencoder network, where the two NMF parts, spectral and spatial matrices, are treated as decoder parameters and hidden outputs, respectively. In the encoding stage, we present a pixelwise fusion model to estimate hidden outputs directly and then reformulate and unfold the model's algorithm to form the encoder network. With the specific architecture, the proposed network is similar to a manifold prior-based model and can be trained patch by patch rather than the entire images. Moreover, we propose an additional unsupervised network to estimate the point spread function and spectral response function. Experimental results conducted on both synthetic and real datasets demonstrate the effectiveness of the proposed approach.