Robust Deep Hyperspectral Imagery Super-Resolution

Abstract

Fusing a low spatial resolution (LR) hyperspectral image (HSI) with a high spatial resolution (HR) multi-spectral image (MSI) is an effective way for HSI super-resolution. When the input LR HSI and the HR MSI are clean, most of existing fusion based methods can produce pleasing results. However, the input HSI and MSI are often corrupted with random noise in practice, which can greatly degrade the performance of these methods. To address this problem, we present a robust deep HSI super-resolution method in this study. In contrast to leveraging a heuristic shallow sparsity or low-rank prior in previous methods, we propose to employ a deep convolution neural network as the prior of the latent HR HSI. With such a prior, the fusion based HSI super-resolution can be formulated as an end-to-end deep learning problem, which can be effectively solved with the back-propagation algorithm. Due to the deep structure, the proposed image prior is able to capture more powerful statistics of the latent HR HSI, and thus can still produce pleasing results with noisy input images. Experimental results on two benchmark datasets demonstrate the effectiveness of the proposed method.