Robust RGB-Guided Super-Resolution of Hyperspectral Images via TV³ Minimization

We consider the problem of increasing the resolution of a hyperspectral image (HSI) with the aid of a high-resolution RGB image of the same scene. The current state-of-the-art algorithms for this task are based on convolutional neural networks (CNNs) and generally assume that the relation between the RGB image and the HSIs remains constant during training and testing. In particular, their performance quickly degrades if we use different color spaces, e.g., CIEXYZ or CIERGB during these stages. In this paper, we propose a method that addresses this problem. Specifically, our method requires no RGB images during training, but still can leverage an RGB image during testing to improve the performance of super-resolution. Furthermore, the method works even if the relation between the RGB and HSI images, captured by the camera spectral response (CSR), is not known precisely. Our experiments demonstrate that the proposed method not only outperforms state-of-the-art methods for joint RGB-HSI super-resolution, but also works for various types of color images.