Icon Colorization Based On Triple Conditional Generative Adversarial Networks

Current automatic colorization systems have many defects such as "contour blur", "color overflow"and "color miscellaneous", especially when they are coloring the images with hollowed-out structure. We propose a model based on triple conditional generative adversarial networks, for generator we provide contour image, colored icon and colorization mask as inputs, our network has three discriminators, structure discriminator is trained to judge if the generated icon has similar contour to the input icon, color discriminator anticipates generated icon and the input icon has the similar color style, the function of mask discriminator is to distinguish whether the output has the similar colorization area to the input mask. For the evaluation, we compared with some existing colorization models, also we made a questionnaire to obtain the evaluation of generated icons from different models. The results showed that our colorization model obtain better results comparing to the other models both in generating hollowed-out and solid structure icons.