Learning to Enhance Visual Quality via Hyperspectral Domain Mapping (Student Abstract)
Keywords:Low-light Image Enhancement, Spectral Profiling, Image-to-image Translation, Generative Adversarial Networks
AbstractDeep 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.
How to Cite
Sinha, H., Mehta, A., Mandal, M., & Narang, P. (2021). Learning to Enhance Visual Quality via Hyperspectral Domain Mapping (Student Abstract). Proceedings of the AAAI Conference on Artificial Intelligence, 35(18), 15895-15896. Retrieved from https://ojs.aaai.org/index.php/AAAI/article/view/17944
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