Recoloring Image For Color Vision Deficiency By GANS

Compared to normal people who can recognize all colors in 3-D color space, people with Color Vision Deficiency (CVD) just could recognize colors in 2-D color space or only in 1-D color space. Therefore, color version deficient people cannot distinguish some colors under certain circumstances. In this paper, a new GAN-based architecture is constructed to improve the color identifiability for color vision deficient people. The improved framework is based on BicycleGAN [1]. First, a CVD simulation module is introduced and integrated into the BicycleGAN [1], allowing us to inspect the recoloring progress from the perspective of people with CVD. Second, a color-to-gray module is added to eliminate the distortion. Third, Gaussian Mixture Model (GMM) [2] and KullbackLeibler (KL) divergence [3], [4] calculation model are inserted as priori information of the conditional GANs [5] to guide the direction of recoloring. Experimental results show that our proposed architecture achieves impressive performance.