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arXiv · 2109.14518

Generative Probabilistic Image Colorization

Chie Furusawa·Shinya Kitaoka·Michael Li·Yuri Odagiri
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Abstract

We propose Generative Probabilistic Image Colorization, a diffusion-based generative process that trains a sequence of probabilistic models to reverse each step of noise corruption. Given a line-drawing image as input, our method suggests multiple candidate colorized images. Therefore, our method accounts for the ill-posed nature of the colorization problem. We conducted comprehensive experiments investigating the colorization of line-drawing images, report the influence of a score-based MCMC approach that corrects the marginal distribution of estimated samples, and further compare different combinations of models and the similarity of their generated images. Despite using only a relatively small training dataset, we experimentally develop a method to generate multiple diverse colorization candidates which avoids mode collapse and does not require any additional constraints, losses, or re-training with alternative training conditions. Our proposed approach performed well not only on color-conditional image generation tasks using biased initial values, but also on some practical image completion and inpainting tasks.

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Chie Furusawa, Shinya Kitaoka, Michael Li, Yuri Odagiri. 2021-09-29. Generative Probabilistic Image Colorization. https://arxiv.org/abs/2109.14518

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