Learning to Enhance Visual Quality via Hyperspectral Domain Mapping (Student Abstract)

No. 18: AAAI-21 Student Papers and Demonstrations

Volume

Proceedings of the AAAI Conference on Artificial Intelligence, 35

AAAI Student Abstract and Poster Program

Deep learning based methods have achieved remarkable success in image restoration and enhancement, but a majority of such methods rely on RGB input images. These methods fail to take into account the rich spectral distribution of natural images. We propose a deep architecture, SpecNet which computes spectral profile to estimate pixel-wise dynamic range adjustment of a given image. First, we employ an unpaired cycle-consistent framework to generate hyperspectral images (HSI) from low-light input images. HSI are further used to generate a normal light image of the same scene. In order to infer a plausible HSI from a RGB image we incorporate a self-supervision and a spectral profile regularization network. We evaluate the benefits of optimizing the spectral profile for real and fake images in low-light conditions on the LOL Dataset.

10.1609/aaai.v35i18.17944