# Separated-Spectral-Distribution Estimation Based on Bayesian Inference with Single RGB Camera

arXiv: https://arxiv.org/abs/2106.01861 · HTML: https://mlsanity.com/paper/2106.01861/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 22 references that are in the map.
- First posted on arXiv 1 Jun 2021.

## Builds on (most cited first, 6 of 6)

- [Learning Intrinsic Image Decomposition from Watching the World](https://mlsanity.com/paper/1804.00582/) (2018)
- [Unsupervised Learning for Intrinsic Image Decomposition from a Single Image](https://mlsanity.com/paper/1911.09930/) (2019)
- [Deep White-Balance Editing](https://mlsanity.com/paper/2004.01354/) (2020)
- [Scene Segmentation-Based Luminance Adjustment for Multi-Exposure Image Fusion](https://mlsanity.com/paper/1903.07428/) (2019)
- [Deep Retinex Network for Estimating Illumination Colors with Self-Supervised Learning](https://mlsanity.com/paper/2102.04003/) (2021)
- [Color Constancy by GANs: An Experimental Survey](https://mlsanity.com/paper/1812.03085/) (2018)

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

- [Template matching with white balance adjustment under multiple illuminants](https://mlsanity.com/paper/2208.02035/) (2022)

## Abstract (arXiv)

In this paper, we propose a novel method for separately estimating spectral distributions from images captured by a typical RGB camera. The proposed method allows us to separately estimate a spectral distribution of illumination, reflectance, or camera sensitivity, while recent hyperspectral cameras are limited to capturing a joint spectral distribution from a scene. In addition, the use of Bayesian inference makes it possible to take into account prior information of both spectral distributions and image noise as probability distributions. As a result, the proposed method can estimate spectral distributions in a unified way, and it can enhance the robustness of the estimation against noise, which conventional spectral-distribution estimation methods cannot. The use of Bayesian inference also enables us to obtain the confidence of estimation results. In an experiment, the proposed method is shown not only to outperform conventional estimation methods in terms of RMSE but also to be robust against noise.