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Cited by 3,178

The DOI matches its Crossref record; 15 of the 15 references it cites were found in a registry or an index.

Colorful Image Colorization

Open access
Zhang, Richard, Isola, Phillip, Efros, Alexei A.·2016·Lecture Notes in Computer Science
DOI 10.1007/978-3-319-46487-9_40
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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.

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Citations

Cites

Research works printed but not linked: 27

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Cited by

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Metadata

Authors
Zhang, R., Isola, P., & Efros, A. A.
Venue
Lecture Notes in Computer Science
Publisher
Springer International Publishing
Published
2016
Pages
649-666
ISSN
  • 0302-9743 (ISSN-L)
  • 1611-3349
DOI
10.1007/978-3-319-46487-9_40
OpenAlex
W2326925005
Cited by
3,178
References
15
FWCI
70.48
Type
conference paper
Language
en
Open access
green
Licence

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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.