Color Constancy by GANs: An Experimental Survey
This work addresses color constancy for computer vision applications, but it is incremental as it surveys existing methods without introducing new techniques.
The paper tackles the color constancy task by formulating it as an image-to-image translation problem using GANs, providing an experimental survey on different GAN types and datasets to offer recommendations for designing CC-GAN architectures.
In this paper, we formulate the color constancy task as an image-to-image translation problem using GANs. By conducting a large set of experiments on different datasets, an experimental survey is provided on the use of different types of GANs to solve for color constancy i.e. CC-GANs (Color Constancy GANs). Based on the experimental review, recommendations are given for the design of CC-GAN architectures based on different criteria, circumstances and datasets.