# Conditional GANs for Multi-Illuminant Color Constancy: Revolution or Yet Another Approach?

arXiv: https://arxiv.org/abs/1811.06604 · HTML: https://mlsanity.com/paper/1811.06604/

## Key facts

- Cited by 17 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 15 Nov 2018.

## Built on by (most cited first, 17 of 17)

- [ShadowFormer: Global Context Helps Image Shadow Removal](https://mlsanity.com/paper/2302.01650/) (2023)
- [Learning from Synthetic Shadows for Shadow Detection and Removal](https://mlsanity.com/paper/2101.01713/) (2021)
- [Instagram Filter Removal on Fashionable Images](https://mlsanity.com/paper/2104.05072/) (2021)
- [Attentive Illumination Decomposition Model for Multi-Illuminant White Balancing](https://mlsanity.com/paper/2402.18277/) (2024)
- [Single-Image Shadow Removal Using Deep Learning: A Comprehensive Survey](https://mlsanity.com/paper/2407.08865/) (2024)
- [Unveiling Deep Shadows: A Survey and Benchmark on Image and Video Shadow Detection, Removal, and Generation in the Deep Learning Era](https://mlsanity.com/paper/2409.02108/) (2024)
- [S3R-Net: A Single-Stage Approach to Self-Supervised Shadow Removal](https://mlsanity.com/paper/2404.12103/) (2024)
- [Patch-wise Contrastive Style Learning for Instagram Filter Removal](https://mlsanity.com/paper/2204.07486/) (2022)
- [Revisiting Image Fusion for Multi-Illuminant White-Balance Correction](https://mlsanity.com/paper/2503.14774/) (2025)
- [Generative Models for Multi-Illumination Color Constancy](https://mlsanity.com/paper/2109.00863/) (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)
- [Learning Physical-Spatio-Temporal Features for Video Shadow Removal](https://mlsanity.com/paper/2303.09370/) (2023)
- [Pixel-Wise Color Constancy via Smoothness Techniques in Multi-Illuminant Scenes](https://mlsanity.com/paper/2402.02922/) (2024)
- [RL-AWB: Deep Reinforcement Learning for Auto White Balance Correction in Low-Light Night-time Scenes](https://mlsanity.com/paper/2601.05249/) (2026)
- [Deep intra-operative illumination calibration of hyperspectral cameras](https://mlsanity.com/paper/2409.07094/) (2024)
- [UnShadowNet: Illumination Critic Guided Contrastive Learning For Shadow Removal](https://mlsanity.com/paper/2203.15441/) (2022)

## Abstract (arXiv)

Non-uniform and multi-illuminant color constancy are important tasks, the solution of which will allow to discard information about lighting conditions in the image. Non-uniform illumination and shadows distort colors of real-world objects and mostly do not contain valuable information. Thus, many computer vision and image processing techniques would benefit from automatic discarding of this information at the pre-processing step. In this work we propose novel view on this classical problem via generative end-to-end algorithm based on image conditioned Generative Adversarial Network. We also demonstrate the potential of the given approach for joint shadow detection and removal. Forced by the lack of training data, we render the largest existing shadow removal dataset and make it publicly available. It consists of approximately 6,000 pairs of wide field of view synthetic images with and without shadows.