Variational Bayes Decomposition for Inverse Estimation with Superimposed Multispectral Intensity

TL;DR

This paper introduces a variational Bayesian inference method for estimating unobservable features from wave intensity data, like X-ray, effectively handling noise through a smooth prior, demonstrated by two experimental results.

Contribution

It presents a novel variational Bayesian approach for inverse estimation in multispectral wave intensity data, incorporating stochastic particle modeling and noise robustness.

Findings

Method accurately estimates unobservable features.

Effective in noisy data scenarios.

Validated by two experimental demonstrations.

Abstract

A variational Bayesian inference for measured wave intensity, such as X-ray intensity, is proposed in this paper. The data is popular to obtain information about unobservable features of an object, such as a material sample and the components of it. The proposed method assumes particles represent the wave, and their behaviors are stochastically modeled. The inference is accurate even if the data is noisy because of a smooth prior setting. Moreover, in this paper, two experimental results show feasibility of the proposed method.