# Color Constancy Using CNNs

arXiv: https://arxiv.org/abs/1504.04548 · HTML: https://mlsanity.com/paper/1504.04548/

## Key facts

- Cited by 21 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 17 Apr 2015.

## Built on by (most cited first, 21 of 21)

- [Attentive Illumination Decomposition Model for Multi-Illuminant White Balancing](https://mlsanity.com/paper/2402.18277/) (2024)
- [GCC: Generative Color Constancy via Diffusing a Color Checker](https://mlsanity.com/paper/2502.17435/) (2025)
- [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)
- [Offset equivariant networks and their applications](https://mlsanity.com/paper/2207.00292/) (2022)
- [Generative Models for Multi-Illumination Color Constancy](https://mlsanity.com/paper/2109.00863/) (2021)
- [Point Cloud Color Constancy](https://mlsanity.com/paper/2111.11280/) (2021)
- [White-Balance First, Adjust Later: Cross-Camera Color Constancy via Vision-Language Evaluation](https://mlsanity.com/paper/2605.19613/) (2026)
- [Leveraging Multispectral Sensors for Color Correction in Mobile Cameras](https://mlsanity.com/paper/2512.08441/) (2025)
- [RL-AWB: Deep Reinforcement Learning for Auto White Balance Correction in Low-Light Night-time Scenes](https://mlsanity.com/paper/2601.05249/) (2026)
- [Fortifying Fully Convolutional Generative Adversarial Networks for Image Super-Resolution Using Divergence Measures](https://mlsanity.com/paper/2404.06294/) (2024)
- [Multi-illuminant Color Constancy via Multi-scale Illuminant Estimation and Fusion](https://mlsanity.com/paper/2502.02021/) (2025)
- [Colour alignment for relative colour constancy via non-standard references](https://mlsanity.com/paper/2112.15106/) (2021)
- [Revisiting and Optimising a CNN Colour Constancy Method for Multi-Illuminant Estimation](https://mlsanity.com/paper/2211.01946/) (2022)
- [Color Recognition in Challenging Lighting Environments: CNN Approach](https://mlsanity.com/paper/2402.04762/) (2024)
- [Boosting Illuminant Estimation in Deep Color Constancy through Enhancing Brightness Robustness](https://mlsanity.com/paper/2502.12418/) (2025)
- [Human-Aligned Evaluation of a Pixel-wise DNN Color Constancy Model](https://mlsanity.com/paper/2602.13887/) (2026)
- [Bio-inspired Color Constancy: From Gray Anchoring Theory to Gray Pixel Methods](https://mlsanity.com/paper/2604.20243/) (2026)
- [Spectrum Aware Illumination Estimation Using Multispectral Image](https://mlsanity.com/paper/2606.14248/) (2026)
- [Illuminant-Adaptive 3D Lookup Tables for Camera Color Correction](https://mlsanity.com/paper/2607.11681/) (2026)
- [Color Pass-Through via Camera-Display Coupling](https://mlsanity.com/paper/2607.12746/) (2026)

## Abstract (arXiv)

In this work we describe a Convolutional Neural Network (CNN) to accurately predict the scene illumination. Taking image patches as input, the CNN works in the spatial domain without using hand-crafted features that are employed by most previous methods. The network consists of one convolutional layer with max pooling, one fully connected layer and three output nodes. Within the network structure, feature learning and regression are integrated into one optimization process, which leads to a more effective model for estimating scene illumination. This approach achieves state-of-the-art performance on a standard dataset of RAW images. Preliminary experiments on images with spatially varying illumination demonstrate the stability of the local illuminant estimation ability of our CNN.