# A Multi-Hypothesis Approach to Color Constancy

arXiv: https://arxiv.org/abs/2002.12896 · HTML: https://mlsanity.com/paper/2002.12896/

## Key facts

- Cited by 18 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 28 Feb 2020.

## Built on by (most cited first, 18 of 18)

- [Model-Based Image Signal Processors via Learnable Dictionaries](https://mlsanity.com/paper/2201.03210/) (2022)
- [NILUT: Conditional Neural Implicit 3D Lookup Tables for Image Enhancement](https://mlsanity.com/paper/2306.11920/) (2023)
- [CLCC: Contrastive Learning for Color Constancy](https://mlsanity.com/paper/2106.04989/) (2021)
- [Auto White-Balance Correction for Mixed-Illuminant Scenes](https://mlsanity.com/paper/2109.08750/) (2021)
- [BSRAW: Improving Blind RAW Image Super-Resolution](https://mlsanity.com/paper/2312.15487/) (2023)
- [CCMNet: Leveraging Calibrated Color Correction Matrices for Cross-Camera Color Constancy](https://mlsanity.com/paper/2504.07959/) (2025)
- [Learning Enriched Illuminants for Cross and Single Sensor Color Constancy](https://mlsanity.com/paper/2203.11068/) (2022)
- [Modeling the Lighting in Scenes as Style for Auto White-Balance Correction](https://mlsanity.com/paper/2210.09090/) (2022)
- [Optimizing Illuminant Estimation in Dual-Exposure HDR Imaging](https://mlsanity.com/paper/2403.02449/) (2024)
- [Revisiting Image Fusion for Multi-Illuminant White-Balance Correction](https://mlsanity.com/paper/2503.14774/) (2025)
- [Test-Time Canonicalization by Foundation Models for Robust Perception](https://mlsanity.com/paper/2507.10375/) (2025)
- [White-Balance First, Adjust Later: Cross-Camera Color Constancy via Vision-Language Evaluation](https://mlsanity.com/paper/2605.19613/) (2026)
- [Deterministic Neural Illumination Mapping for Efficient Auto-White Balance Correction](https://mlsanity.com/paper/2308.03939/) (2023)
- [RL-AWB: Deep Reinforcement Learning for Auto White Balance Correction in Low-Light Night-time Scenes](https://mlsanity.com/paper/2601.05249/) (2026)
- [Off the Planckian Locus: Using 2D Chromaticity to Improve In-Camera Color](https://mlsanity.com/paper/2511.17133/) (2025)
- [Practical cross-sensor color constancy using a dual-mapping strategy](https://mlsanity.com/paper/2311.11773/) (2023)
- [Boosting Illuminant Estimation in Deep Color Constancy through Enhancing Brightness Robustness](https://mlsanity.com/paper/2502.12418/) (2025)
- [Learning Camera-Agnostic White-Balance Preferences](https://mlsanity.com/paper/2507.01342/) (2025)

## Abstract (arXiv)

Contemporary approaches frame the color constancy problem as learning camera specific illuminant mappings. While high accuracy can be achieved on camera specific data, these models depend on camera spectral sensitivity and typically exhibit poor generalisation to new devices. Additionally, regression methods produce point estimates that do not explicitly account for potential ambiguities among plausible illuminant solutions, due to the ill-posed nature of the problem. We propose a Bayesian framework that naturally handles color constancy ambiguity via a multi-hypothesis strategy. Firstly, we select a set of candidate scene illuminants in a data-driven fashion and apply them to a target image to generate of set of corrected images. Secondly, we estimate, for each corrected image, the likelihood of the light source being achromatic using a camera-agnostic CNN. Finally, our method explicitly learns a final illumination estimate from the generated posterior probability distribution. Our likelihood estimator learns to answer a camera-agnostic question and thus enables effective multi-camera training by disentangling illuminant estimation from the supervised learning task. We extensively evaluate our proposed approach and additionally set a benchmark for novel sensor generalisation without re-training. Our method provides state-of-the-art accuracy on multiple public datasets (up to 11% median angular error improvement) while maintaining real-time execution.