# Learning Camera-Agnostic White-Balance Preferences

arXiv: https://arxiv.org/abs/2507.01342 · HTML: https://mlsanity.com/paper/2507.01342/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 21 of its 51 references that are in the map.
- First posted on arXiv 2 Jul 2025.

## Builds on (most cited first, 21 of 21)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [SGDR: Stochastic Gradient Descent with Warm Restarts](https://mlsanity.com/paper/1608.03983/) (2016)
- [Fast and Accurate Deep Network Learning by Exponential Linear Units (ELUs)](https://mlsanity.com/paper/1511.07289/) (2015)
- [Mobile Computational Photography: A Tour](https://mlsanity.com/paper/2102.09000/) (2021)
- [Deep White-Balance Editing](https://mlsanity.com/paper/2004.01354/) (2020)
- [Fast Fourier Color Constancy](https://mlsanity.com/paper/1611.07596/) (2016)
- [Convolutional Color Constancy](https://mlsanity.com/paper/1507.00410/) (2015)
- [Cross-Camera Convolutional Color Constancy](https://mlsanity.com/paper/2011.11890/) (2020)
- [CIE XYZ Net: Unprocessing Images for Low-Level Computer Vision Tasks](https://mlsanity.com/paper/2006.12709/) (2020)
- [CLCC: Contrastive Learning for Color Constancy](https://mlsanity.com/paper/2106.04989/) (2021)
- [Exposure: A White-Box Photo Post-Processing Framework](https://mlsanity.com/paper/1709.09602/) (2017)
- [On Finding Gray Pixels](https://mlsanity.com/paper/1901.03198/) (2019)
- [Cascading Convolutional Color Constancy](https://mlsanity.com/paper/1912.11180/) (2019)
- [Sensor-Independent Illumination Estimation for DNN Models](https://mlsanity.com/paper/1912.06888/) (2019)
- [A Multi-Hypothesis Approach to Color Constancy](https://mlsanity.com/paper/2002.12896/) (2020)
- [Semi-Supervised Raw-to-Raw Mapping](https://mlsanity.com/paper/2106.13883/) (2021)
- [Bag of Color Features For Color Constancy](https://mlsanity.com/paper/1906.04445/) (2019)
- [Time-Aware Auto White Balance in Mobile Photography](https://mlsanity.com/paper/2504.05623/) (2025)
- [CCMNet: Leveraging Calibrated Color Correction Matrices for Cross-Camera Color Constancy](https://mlsanity.com/paper/2504.07959/) (2025)
- [Interactive White Balancing for Camera-Rendered Images](https://mlsanity.com/paper/2009.12632/) (2020)
- [Optimizing Illuminant Estimation in Dual-Exposure HDR Imaging](https://mlsanity.com/paper/2403.02449/) (2024)

## Abstract (arXiv)

The image signal processor (ISP) pipeline in modern cameras consists of several modules that transform raw sensor data into visually pleasing images in a display color space. Among these, the auto white balance (AWB) module is essential for compensating for scene illumination. However, commercial AWB systems often strive to compute aesthetic white-balance preferences rather than accurate neutral color correction. While learning-based methods have improved AWB accuracy, they typically struggle to generalize across different camera sensors -- an issue for smartphones with multiple cameras. Recent work has explored cross-camera AWB, but most methods remain focused on achieving neutral white balance. In contrast, this paper is the first to address aesthetic consistency by learning a post-illuminant-estimation mapping that transforms neutral illuminant corrections into aesthetically preferred corrections in a camera-agnostic space. Once trained, our mapping can be applied after any neutral AWB module to enable consistent and stylized color rendering across unseen cameras. Our proposed model is lightweight -- containing only ~500 parameters -- and runs in just 0.024 milliseconds on a typical flagship mobile CPU. Evaluated on a dataset of 771 smartphone images from three different cameras, our method achieves state-of-the-art performance while remaining fully compatible with existing cross-camera AWB techniques, introducing minimal computational and memory overhead.