  Deep learning based methods have achieved remarkable success in image
restoration and enhancement, but most 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 is further used to
generate a normal light image of the same scene. We incorporate a
self-supervision and a spectral profile regularization network to infer a
plausible HSI from an RGB image. We evaluate the benefits of optimizing the
spectral profile for real and fake images in low-light conditions on the LOL
Dataset.
