Learning Representations for Automatic Colorization

Gustav Larsson; Michael Maire; Gregory Shakhnarovich

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arXiv · 1603.06668

Learning Representations for Automatic Colorization

Gustav Larsson·Michael Maire·Gregory Shakhnarovich

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Abstract

We develop a fully automatic image colorization system. Our approach leverages recent advances in deep networks, exploiting both low-level and semantic representations. As many scene elements naturally appear according to multimodal color distributions, we train our model to predict per-pixel color histograms. This intermediate output can be used to automatically generate a color image, or further manipulated prior to image formation. On both fully and partially automatic colorization tasks, we outperform existing methods. We also explore colorization as a vehicle for self-supervised visual representation learning.

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Gustav Larsson, Michael Maire, Gregory Shakhnarovich. 2016-03-22. Learning Representations for Automatic Colorization. https://arxiv.org/abs/1603.06668

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