# Learning Enriched Illuminants for Cross and Single Sensor Color Constancy

arXiv: https://arxiv.org/abs/2203.11068 · HTML: https://mlsanity.com/paper/2203.11068/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Builds on 34 of its 61 references that are in the map.
- First posted on arXiv 21 Mar 2022.

## Builds on (most cited first, 30 of 34)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [Microsoft COCO: Common Objects in Context](https://mlsanity.com/paper/1405.0312/) (2014)
- [A Simple Framework for Contrastive Learning of Visual Representations](https://mlsanity.com/paper/2002.05709/) (2020)
- [Momentum Contrast for Unsupervised Visual Representation Learning](https://mlsanity.com/paper/1911.05722/) (2019)
- [Squeeze-and-Excitation Networks](https://mlsanity.com/paper/1709.01507/) (2017)
- [MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications](https://mlsanity.com/paper/1704.04861/) (2017)
- [CBAM: Convolutional Block Attention Module](https://mlsanity.com/paper/1807.06521/) (2018)
- [Non-local Neural Networks](https://mlsanity.com/paper/1711.07971/) (2017)
- [ShuffleNet: An Extremely Efficient Convolutional Neural Network for Mobile Devices](https://mlsanity.com/paper/1707.01083/) (2017)
- [Colorful Image Colorization](https://mlsanity.com/paper/1603.08511/) (2016)
- [SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and <0.5MB model size](https://mlsanity.com/paper/1602.07360/) (2016)
- [Generative Image Inpainting with Contextual Attention](https://mlsanity.com/paper/1801.07892/) (2018)
- [A Survey of Model Compression and Acceleration for Deep Neural Networks](https://mlsanity.com/paper/1710.09282/) (2017)
- [Unprocessing Images for Learned Raw Denoising](https://mlsanity.com/paper/1811.11127/) (2018)
- [CycleISP: Real Image Restoration via Improved Data Synthesis](https://mlsanity.com/paper/2003.07761/) (2020)
- [Dual Residual Networks Leveraging the Potential of Paired Operations for Image Restoration](https://mlsanity.com/paper/1903.08817/) (2019)
- [Fast Fourier Color Constancy](https://mlsanity.com/paper/1611.07596/) (2016)
- [What Else Can Fool Deep Learning? Addressing Color Constancy Errors on Deep Neural Network Performance](https://mlsanity.com/paper/1912.06960/) (2019)
- [Convolutional Color Constancy](https://mlsanity.com/paper/1507.00410/) (2015)
- [On Finding Gray Pixels](https://mlsanity.com/paper/1901.03198/) (2019)
- [Cascading Convolutional Color Constancy](https://mlsanity.com/paper/1912.11180/) (2019)
- [Color Constancy Using CNNs](https://mlsanity.com/paper/1504.04548/) (2015)
- [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)
- [Approaching the Computational Color Constancy as a Classification Problem through Deep Learning](https://mlsanity.com/paper/1608.07951/) (2016)
- [Towards Understanding the Importance of Noise in Training Neural Networks](https://mlsanity.com/paper/1909.03172/) (2019)
- [INTEL-TAU: A Color Constancy Dataset](https://mlsanity.com/paper/1910.10404/) (2019)

## Built on by (most cited first, 7 of 7)

- [RL-AWB: Deep Reinforcement Learning for Auto White Balance Correction in Low-Light Night-time Scenes](https://mlsanity.com/paper/2601.05249/) (2026)
- [A Comprehensive Survey on Image Signal Processing Approaches for Low-Illumination Image Enhancement](https://mlsanity.com/paper/2502.05995/) (2025)
- [Boosting Illuminant Estimation in Deep Color Constancy through Enhancing Brightness Robustness](https://mlsanity.com/paper/2502.12418/) (2025)
- [Deeply Dual Supervised learning for melanoma recognition](https://mlsanity.com/paper/2508.01994/) (2025)
- [Edge Detection for Organ Boundaries via Top Down Refinement and SubPixel Upsampling](https://mlsanity.com/paper/2508.06805/) (2025)
- [DualResolution Residual Architecture with Artifact Suppression for Melanocytic Lesion Segmentation](https://mlsanity.com/paper/2508.06816/) (2025)
- [VesselRW: Weakly Supervised Subcutaneous Vessel Segmentation via Learned Random Walk Propagation](https://mlsanity.com/paper/2508.06819/) (2025)

## Abstract (arXiv)

Color constancy aims to restore the constant colors of a scene under different illuminants. However, due to the existence of camera spectral sensitivity, the network trained on a certain sensor, cannot work well on others. Also, since the training datasets are collected in certain environments, the diversity of illuminants is limited for complex real world prediction. In this paper, we tackle these problems via two aspects. First, we propose cross-sensor self-supervised training to train the network. In detail, we consider both the general sRGB images and the white-balanced RAW images from current available datasets as the white-balanced agents. Then, we train the network by randomly sampling the artificial illuminants in a sensor-independent manner for scene relighting and supervision. Second, we analyze a previous cascaded framework and present a more compact and accurate model by sharing the backbone parameters with learning attention specifically. Experiments show that our cross-sensor model and single-sensor model outperform other state-of-the-art methods by a large margin on cross and single sensor evaluations, respectively, with only 16% parameters of the previous best model.