Spectral Super-Resolution Using CNN Decomposing a Color Image into Luminance and Chrominance Components

Abstract

Hyper-spectral images are used in a wide range of fields such as industry, medicine, remote sensing, and so on. They are also used in computer graphics as light probe images and textures in spectral rendering. The acquisition of spectral images is, however, costly in terms of equipment and time, which hinders its acquisition and use. Conventional spectral super-resolutions using deep learning have been adopting a direct end-to-end learning method to RGB and hyper-spectral images. In contrast, we focus on the fact that hyper-spectral images are decomposed into luminance and chrominance components, and we propose a novel spectral super-resolution using a deep learning to estimate each component separately. Finally, in the proposed method, a hyper-spectral image is reconstructed by combining the estimated luminance and chrominance components.