# Improved Mapping Between Illuminations and Sensors for RAW Images

arXiv: https://arxiv.org/abs/2508.14730 · HTML: https://mlsanity.com/paper/2508.14730/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 19 references that are in the map.
- First posted on arXiv 20 Aug 2025.

## Builds on (most cited first, 8 of 8)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [You Only Look Once: Unified, Real-Time Object Detection](https://mlsanity.com/paper/1506.02640/) (2015)
- [Unsupervised Representation Learning by Sorting Sequences](https://mlsanity.com/paper/1708.01246/) (2017)
- [CameraNet: A Two-Stage Framework for Effective Camera ISP Learning](https://mlsanity.com/paper/1908.01481/) (2019)
- [CLCC: Contrastive Learning for Color Constancy](https://mlsanity.com/paper/2106.04989/) (2021)
- [How useful is photo-realistic rendering for visual learning?](https://mlsanity.com/paper/1603.08152/) (2016)
- [Cascading Convolutional Color Constancy](https://mlsanity.com/paper/1912.11180/) (2019)

## Built on by (most cited first, 2 of 2)

- [Off the Planckian Locus: Using 2D Chromaticity to Improve In-Camera Color](https://mlsanity.com/paper/2511.17133/) (2025)
- [Illuminant-Adaptive 3D Lookup Tables for Camera Color Correction](https://mlsanity.com/paper/2607.11681/) (2026)

## Abstract (arXiv)

RAW images are unprocessed camera sensor output with sensor-specific RGB values based on the sensor's color filter spectral sensitivities. RAW images also incur strong color casts due to the sensor's response to the spectral properties of scene illumination. The sensor- and illumination-specific nature of RAW images makes it challenging to capture RAW datasets for deep learning methods, as scenes need to be captured for each sensor and under a wide range of illumination. Methods for illumination augmentation for a given sensor and the ability to map RAW images between sensors are important for reducing the burden of data capture. To explore this problem, we introduce the first-of-its-kind dataset comprising carefully captured scenes under a wide range of illumination. Specifically, we use a customized lightbox with tunable illumination spectra to capture several scenes with different cameras. Our illumination and sensor mapping dataset has 390 illuminations, four cameras, and 18 scenes. Using this dataset, we introduce a lightweight neural network approach for illumination and sensor mapping that outperforms competing methods. We demonstrate the utility of our approach on the downstream task of training a neural ISP. Link to project page: https://github.com/SamsungLabs/illum-sensor-mapping.