# Cascading Convolutional Color Constancy

arXiv: https://arxiv.org/abs/1912.11180 · HTML: https://mlsanity.com/paper/1912.11180/

## Key facts

- Cited by 22 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 24 Dec 2019.

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

- [CLCC: Contrastive Learning for Color Constancy](https://mlsanity.com/paper/2106.04989/) (2021)
- [Spatial-Separated Curve Rendering Network for Efficient and High-Resolution Image Harmonization](https://mlsanity.com/paper/2109.05750/) (2021)
- [Time-Aware Auto White Balance in Mobile Photography](https://mlsanity.com/paper/2504.05623/) (2025)
- [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)
- [Optimizing Illuminant Estimation in Dual-Exposure HDR Imaging](https://mlsanity.com/paper/2403.02449/) (2024)
- [Point Cloud Color Constancy](https://mlsanity.com/paper/2111.11280/) (2021)
- [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)
- [Improved Mapping Between Illuminations and Sensors for RAW Images](https://mlsanity.com/paper/2508.14730/) (2025)
- [RL-AWB: Deep Reinforcement Learning for Auto White Balance Correction in Low-Light Night-time Scenes](https://mlsanity.com/paper/2601.05249/) (2026)
- [Cascading Convolutional Temporal Colour Constancy](https://mlsanity.com/paper/2106.07955/) (2021)
- [Beyond Calibration: Physically Informed Learning for Raw-to-Raw Mapping](https://mlsanity.com/paper/2506.08650/) (2025)
- [Colour alignment for relative colour constancy via non-standard references](https://mlsanity.com/paper/2112.15106/) (2021)
- [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)
- [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)
- [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)

Regressing the illumination of a scene from the representations of object appearances is popularly adopted in computational color constancy. However, it's still challenging due to intrinsic appearance and label ambiguities caused by unknown illuminants, diverse reflection property of materials and extrinsic imaging factors (such as different camera sensors). In this paper, we introduce a novel algorithm by Cascading Convolutional Color Constancy (in short, C4) to improve robustness of regression learning and achieve stable generalization capability across datasets (different cameras and scenes) in a unique framework. The proposed C4 method ensembles a series of dependent illumination hypotheses from each cascade stage via introducing a weighted multiply-accumulate loss function, which can inherently capture different modes of illuminations and explicitly enforce coarse-to-fine network optimization. Experimental results on the public Color Checker and NUS 8-Camera benchmarks demonstrate superior performance of the proposed algorithm in comparison with the state-of-the-art methods, especially for more difficult scenes.