Multimodal ambiguity and color bleeding remain challenging in colorization. To tackle these problems, we propose a new GAN-based colorization approach PalGAN, integrated with palette estimation and chromatic attention. To circumvent the multimodality issue, we present a new colorization formulation that estimates a probabilistic palette from the input gray image first, then conducts color assignment conditioned on the palette through a generative model. Further, we handle color bleeding with chromatic attention. It studies color affinities by considering both semantic and intensity correlation. In extensive experiments, PalGAN outperforms state-of-the-arts in quantitative evaluation and visual comparison, delivering notable diverse, contrastive, and edge-preserving appearances. With the palette design, our method enables color transfer between images even with irrelevant contexts.
@article{arxiv.2210.11204,
title = {PalGAN: Image Colorization with Palette Generative Adversarial Networks},
author = {Yi Wang and Menghan Xia and Lu Qi and Jing Shao and Yu Qiao},
journal= {arXiv preprint arXiv:2210.11204},
year = {2022}
}