# Optimizing Illuminant Estimation in Dual-Exposure HDR Imaging

arXiv: https://arxiv.org/abs/2403.02449 · HTML: https://mlsanity.com/paper/2403.02449/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 22 of its 65 references that are in the map.
- First posted on arXiv 4 Mar 2024.

## Builds on (most cited first, 22 of 22)

- [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)
- [Empirical Evaluation of Rectified Activations in Convolutional Network](https://mlsanity.com/paper/1505.00853/) (2015)
- [Mobile Computational Photography: A Tour](https://mlsanity.com/paper/2102.09000/) (2021)
- [Handheld Mobile Photography in Very Low Light](https://mlsanity.com/paper/1910.11336/) (2019)
- [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)
- [CLCC: Contrastive Learning for Color Constancy](https://mlsanity.com/paper/2106.04989/) (2021)
- [On Finding Gray Pixels](https://mlsanity.com/paper/1901.03198/) (2019)
- [Cascading Convolutional Color Constancy](https://mlsanity.com/paper/1912.11180/) (2019)
- [Single-Image HDR Reconstruction by Multi-Exposure Generation](https://mlsanity.com/paper/2210.15897/) (2022)
- [Unsupervised Learning for Color Constancy](https://mlsanity.com/paper/1712.00436/) (2017)
- [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)
- [Approaching the Computational Color Constancy as a Classification Problem through Deep Learning](https://mlsanity.com/paper/1608.07951/) (2016)
- [Bag of Color Features For Color Constancy](https://mlsanity.com/paper/1906.04445/) (2019)
- [Point Cloud Color Constancy](https://mlsanity.com/paper/2111.11280/) (2021)
- [Spectral Sensitivity Estimation Without a Camera](https://mlsanity.com/paper/2304.11549/) (2023)
- [Robust channel-wise illumination estimation](https://mlsanity.com/paper/2111.05681/) (2021)
- [Color Homography Color Correction](https://mlsanity.com/paper/1607.05947/) (2016)

## Built on by (most cited first, 5 of 5)

- [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)
- [White-Balance First, Adjust Later: Cross-Camera Color Constancy via Vision-Language Evaluation](https://mlsanity.com/paper/2605.19613/) (2026)
- [RL-AWB: Deep Reinforcement Learning for Auto White Balance Correction in Low-Light Night-time Scenes](https://mlsanity.com/paper/2601.05249/) (2026)
- [Learning Camera-Agnostic White-Balance Preferences](https://mlsanity.com/paper/2507.01342/) (2025)

## Abstract (arXiv)

High dynamic range (HDR) imaging involves capturing a series of frames of the same scene, each with different exposure settings, to broaden the dynamic range of light. This can be achieved through burst capturing or using staggered HDR sensors that capture long and short exposures simultaneously in the camera image signal processor (ISP). Within camera ISP pipeline, illuminant estimation is a crucial step aiming to estimate the color of the global illuminant in the scene. This estimation is used in camera ISP white-balance module to remove undesirable color cast in the final image. Despite the multiple frames captured in the HDR pipeline, conventional illuminant estimation methods often rely only on a single frame of the scene. In this paper, we explore leveraging information from frames captured with different exposure times. Specifically, we introduce a simple feature extracted from dual-exposure images to guide illuminant estimators, referred to as the dual-exposure feature (DEF). To validate the efficiency of DEF, we employed two illuminant estimators using the proposed DEF: 1) a multilayer perceptron network (MLP), referred to as exposure-based MLP (EMLP), and 2) a modified version of the convolutional color constancy (CCC) to integrate our DEF, that we call ECCC. Both EMLP and ECCC achieve promising results, in some cases surpassing prior methods that require hundreds of thousands or millions of parameters, with only a few hundred parameters for EMLP and a few thousand parameters for ECCC.