# Automatic Illumination Spectrum Recovery

arXiv: https://arxiv.org/abs/2305.19538 · HTML: https://mlsanity.com/paper/2305.19538/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 2 of its 4 references that are in the map.
- First posted on arXiv 31 May 2023.

## Builds on (most cited first, 2 of 2)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)

## Built on by (most cited first, 1 of 1)

- [A General Purpose Spectral Foundational Model for Both Proximal and Remote Sensing Spectral Imaging](https://mlsanity.com/paper/2503.01628/) (2025)

## Abstract (arXiv)

We develop a deep learning network to estimate the illumination spectrum of hyperspectral images under various lighting conditions. To this end, a dataset, IllumNet, was created. Images were captured using a Specim IQ camera under various illumination conditions, both indoor and outdoor. Outdoor images were captured in sunny, overcast, and shady conditions and at different times of the day. For indoor images, halogen and LED light sources were used, as well as mixed light sources, mainly halogen or LED and fluorescent. The ResNet18 network was employed in this study, but with the 2D kernel changed to a 3D kernel to suit the spectral nature of the data. As well as fitting the actual illumination spectrum well, the predicted illumination spectrum should also be smooth, and this is achieved by the cubic smoothing spline error cost function. Experimental results indicate that the trained model can infer an accurate estimate of the illumination spectrum.