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

TL;DR

This paper introduces a Bayesian inference-based method to separately estimate spectral distributions of illumination, reflectance, or camera sensitivity from standard RGB images, improving robustness and confidence over traditional joint spectral estimation methods.

Contribution

It presents a novel approach for separate spectral distribution estimation from RGB images using Bayesian inference, which enhances robustness and provides confidence measures.

Findings

Outperforms conventional methods in RMSE

Robust against image noise

Provides confidence in estimates

Abstract

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.