# Auto White-Balance Correction for Mixed-Illuminant Scenes

arXiv: https://arxiv.org/abs/2109.08750 · HTML: https://mlsanity.com/paper/2109.08750/

## Key facts

- Cited by 22 papers in the map (data through 14 Oct 2026).
- Builds on 16 of its 64 references that are in the map.
- First posted on arXiv 17 Sep 2021.

## Builds on (most cited first, 16 of 16)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Context-aware Synthesis for Video Frame Interpolation](https://mlsanity.com/paper/1803.10967/) (2018)
- [The Fast Bilateral Solver](https://mlsanity.com/paper/1511.03296/) (2015)
- [Residual Conv-Deconv Grid Network for Semantic Segmentation](https://mlsanity.com/paper/1707.07958/) (2017)
- [Deep White-Balance Editing](https://mlsanity.com/paper/2004.01354/) (2020)
- [A Dataset of Multi-Illumination Images in the Wild](https://mlsanity.com/paper/1910.08131/) (2019)
- [Fast Fourier Color Constancy](https://mlsanity.com/paper/1611.07596/) (2016)
- [What Else Can Fool Deep Learning? Addressing Color Constancy Errors on Deep Neural Network Performance](https://mlsanity.com/paper/1912.06960/) (2019)
- [Convolutional Color Constancy](https://mlsanity.com/paper/1507.00410/) (2015)
- [Cross-Camera Convolutional Color Constancy](https://mlsanity.com/paper/2011.11890/) (2020)
- [CLCC: Contrastive Learning for Color Constancy](https://mlsanity.com/paper/2106.04989/) (2021)
- [On Finding Gray Pixels](https://mlsanity.com/paper/1901.03198/) (2019)
- [Unsupervised Learning for Color Constancy](https://mlsanity.com/paper/1712.00436/) (2017)
- [A Multi-Hypothesis Approach to Color Constancy](https://mlsanity.com/paper/2002.12896/) (2020)
- [Approaching the Computational Color Constancy as a Classification Problem through Deep Learning](https://mlsanity.com/paper/1608.07951/) (2016)
- [Interactive White Balancing for Camera-Rendered Images](https://mlsanity.com/paper/2009.12632/) (2020)

## Built on by (most cited first, 22 of 22)

- [You Only Need 90K Parameters to Adapt Light: A Light Weight Transformer for Image Enhancement and Exposure Correction](https://mlsanity.com/paper/2205.14871/) (2022)
- [Text-DiFuse: An Interactive Multi-Modal Image Fusion Framework based on Text-modulated Diffusion Model](https://mlsanity.com/paper/2410.23905/) (2024)
- [Beyond the Pixel: a Photometrically Calibrated HDR Dataset for Luminance and Color Prediction](https://mlsanity.com/paper/2304.12372/) (2023)
- [Attentive Illumination Decomposition Model for Multi-Illuminant White Balancing](https://mlsanity.com/paper/2402.18277/) (2024)
- [Modeling the Lighting in Scenes as Style for Auto White-Balance Correction](https://mlsanity.com/paper/2210.09090/) (2022)
- [Revisiting Image Fusion for Multi-Illuminant White-Balance Correction](https://mlsanity.com/paper/2503.14774/) (2025)
- [MIMT: Multi-Illuminant Color Constancy via Multi-Task Local Surface and Light Color Learning](https://mlsanity.com/paper/2211.08772/) (2022)
- [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)
- [Zero-Shot Low Light Image Enhancement with Diffusion Prior](https://mlsanity.com/paper/2412.13401/) (2024)
- [Leveraging Multispectral Sensors for Color Correction in Mobile Cameras](https://mlsanity.com/paper/2512.08441/) (2025)
- [Perception-Inspired Color Space Design for Photo White Balance Editing](https://mlsanity.com/paper/2512.09383/) (2025)
- [RL-AWB: Deep Reinforcement Learning for Auto White Balance Correction in Low-Light Night-time Scenes](https://mlsanity.com/paper/2601.05249/) (2026)
- [Investigating Color Illusions from the Perspective of Computational Color Constancy](https://mlsanity.com/paper/2312.13114/) (2023)
- [WB LUTs: Contrastive Learning for White Balancing Lookup Tables](https://mlsanity.com/paper/2404.10133/) (2024)
- [Automatic Spectral Calibration of Hyperspectral Images:Method, Dataset and Benchmark](https://mlsanity.com/paper/2412.14925/) (2024)
- [Parameter Blending for Multi-Camera Harmonization for Automotive Surround View Systems](https://mlsanity.com/paper/2406.11066/) (2024)
- [A Comprehensive Survey on Image Signal Processing Approaches for Low-Illumination Image Enhancement](https://mlsanity.com/paper/2502.05995/) (2025)
- [Human-Aligned Evaluation of a Pixel-wise DNN Color Constancy Model](https://mlsanity.com/paper/2602.13887/) (2026)
- [Color Constancy in Hyperspectral Imaging via Reduced Spectral Spaces](https://mlsanity.com/paper/2605.13306/) (2026)
- [Color Pass-Through via Camera-Display Coupling](https://mlsanity.com/paper/2607.12746/) (2026)
- [SAFE: Scene-Aware Feature Modulation for Color Constancy with Learned Color Space in Pure-Color Scenes](https://mlsanity.com/paper/2608.13967/) (2026)

## Abstract (arXiv)

Auto white balance (AWB) is applied by camera hardware at capture time to remove the color cast caused by the scene illumination. The vast majority of white-balance algorithms assume a single light source illuminates the scene; however, real scenes often have mixed lighting conditions. This paper presents an effective AWB method to deal with such mixed-illuminant scenes. A unique departure from conventional AWB, our method does not require illuminant estimation, as is the case in traditional camera AWB modules. Instead, our method proposes to render the captured scene with a small set of predefined white-balance settings. Given this set of rendered images, our method learns to estimate weighting maps that are used to blend the rendered images to generate the final corrected image. Through extensive experiments, we show this proposed method produces promising results compared to other alternatives for single- and mixed-illuminant scene color correction. Our source code and trained models are available at https://github.com/mahmoudnafifi/mixedillWB.