# Probabilistic Image Colorization

arXiv: https://arxiv.org/abs/1705.04258 · HTML: https://mlsanity.com/paper/1705.04258/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 11 May 2017.

## Built on by (most cited first, 13 of 13)

- [Palette: Image-to-Image Diffusion Models](https://mlsanity.com/paper/2111.05826/) (2021)
- [Colorization Transformer](https://mlsanity.com/paper/2102.04432/) (2021)
- [UViM: A Unified Modeling Approach for Vision with Learned Guiding Codes](https://mlsanity.com/paper/2205.10337/) (2022)
- [Towards Vivid and Diverse Image Colorization with Generative Color Prior](https://mlsanity.com/paper/2108.08826/) (2021)
- [VCGAN: Video Colorization with Hybrid Generative Adversarial Network](https://mlsanity.com/paper/2104.12357/) (2021)
- [SVCNet: Scribble-based Video Colorization Network with Temporal Aggregation](https://mlsanity.com/paper/2303.11591/) (2023)
- [Generative Probabilistic Image Colorization](https://mlsanity.com/paper/2109.14518/) (2021)
- [Analysis of Different Losses for Deep Learning Image Colorization](https://mlsanity.com/paper/2204.02980/) (2022)
- [An Analysis of Generative Methods for Multiple Image Inpainting](https://mlsanity.com/paper/2205.02146/) (2022)
- [Influence of Color Spaces for Deep Learning Image Colorization](https://mlsanity.com/paper/2204.02850/) (2022)
- [Human vs Objective Evaluation of Colourisation Performance](https://mlsanity.com/paper/2204.05200/) (2022)
- [ParaColorizer: Realistic Image Colorization using Parallel Generative Networks](https://mlsanity.com/paper/2208.08295/) (2022)
- [Computer-aided Colorization State-of-the-science: A Survey](https://mlsanity.com/paper/2410.02288/) (2024)

## Abstract (arXiv)

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. We demonstrate strong quantitative and qualitative experimental results on the CIFAR-10 dataset and the challenging ILSVRC 2012 dataset.