Probabilistic Image Colorization

Amelie Royer, Alexander Kolesnikov and Christoph Lampert

Abstract

We develop a probabilistic technique for colorizing grayscale natural images. In light of the intrinsic uncertainty of this task, the proposed probabilistic framework has numerous desirable properties. In particular, our model is able to produce multiple plausible and vivid colorizations for a given grayscale image and is one of the first colorization models to provide a proper stochastic sampling scheme. Moreover, our training procedure is supported by a rigorous theoretical framework that does not require any ad hoc heuristics and allows for efficient modeling and learning of the joint pixel color distribution.

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DOI

10.5244/C.31.85
https://dx.doi.org/10.5244/C.31.85

Citation

Amelie Royer, Alexander Kolesnikov and Christoph Lampert. Probabilistic Image Colorization. In T.K. Kim, S. Zafeiriou, G. Brostow and K. Mikolajczyk, editors, Proceedings of the British Machine Vision Conference (BMVC), pages 85.1-85.12. BMVA Press, September 2017.

Bibtex

            @inproceedings{BMVC2017_85,
                title={Probabilistic Image Colorization},
                author={Amelie Royer, Alexander Kolesnikov and Christoph Lampert},
                year={2017},
                month={September},
                pages={85.1-85.12},
                articleno={85},
                numpages={12},
                booktitle={Proceedings of the British Machine Vision Conference (BMVC)},
                publisher={BMVA Press},
                editor={Tae-Kyun Kim, Stefanos Zafeiriou, Gabriel Brostow and Krystian Mikolajczyk},
                doi={10.5244/C.31.85},
                isbn={1-901725-60-X},
                url={https://dx.doi.org/10.5244/C.31.85}
            }