Personalized Image Generation for Color Vision Deficiency Population

Approximately, 350 million people, a proportion of 8%, suffer from color vision deficiency (CVD). While image generation algorithms have been highly successful in synthesizing high-quality images, CVD populations are unintentionally excluded from target users and have difficulties understanding the generated images as normal viewers do. Although a straightforward baseline can be formed by combining generation models and recolor compensation methods as the post-processing, the CVD friendliness of the result images is still limited since the input image content of recolor methods is not CVD-oriented and will keep fixed during the recolor compensation process. Besides, the CVD populations can't be fully served since the varying degrees of CVD are often neglected in recoloring methods. Instead, we propose a personalized CVD-friendly image generation algorithm with two key characteristics: (i) generating CVD-oriented images end-to-end; (ii) generating continuous personalized images for people with various CVD types and degrees through disentangling the color representation based on a triple-latent structure. Quantitative experiments and the user study indicate our proposed image generation model can generate practical and compelling results compared to the normal generation model and combination baselines on several datasets.