Colorful Image Colorization

TL;DR

This paper presents a fully automatic, CNN-based method for vibrant image colorization that produces realistic results and enhances self-supervised feature learning, outperforming previous approaches in human perception tests.

Contribution

It introduces a classification-based approach with class rebalancing for diverse, realistic colorization and demonstrates its effectiveness as a pretext task for feature learning.

Findings

Successfully fools humans in 32% of colorization Turing tests

Achieves state-of-the-art results in self-supervised feature learning benchmarks

Produces vibrant, realistic colorizations without user interaction

Abstract

Given a grayscale photograph as input, this paper attacks the problem of hallucinating a plausible color version of the photograph. This problem is clearly underconstrained, so previous approaches have either relied on significant user interaction or resulted in desaturated colorizations. We propose a fully automatic approach that produces vibrant and realistic colorizations. We embrace the underlying uncertainty of the problem by posing it as a classification task and use class-rebalancing at training time to increase the diversity of colors in the result. The system is implemented as a feed-forward pass in a CNN at test time and is trained on over a million color images. We evaluate our algorithm using a "colorization Turing test," asking human participants to choose between a generated and ground truth color image. Our method successfully fools humans on 32% of the trials, significantly higher than previous methods. Moreover, we show that colorization can be a powerful pretext task for self-supervised feature learning, acting as a cross-channel encoder. This approach results in state-of-the-art performance on several feature learning benchmarks.