用变分贝叶斯分解逆估计叠加多光谱强度，提升噪声下成像精度
Variational Bayes Decomposition for Inverse Estimation with Superimposed Multispectral Intensity
- 将波粒行为随机建模，通过变分贝叶斯推断逆估计强度分布
- 在噪声数据下仍保持高精度，得益于平滑先验设定
- 已在两组实验中验证可行性，适合材料成分反演场景
本文提出一种针对测量波强度（如X射线强度）的变分贝叶斯推断方法。该数据常用于获取物体不可观测特征的信息，如材料样品及其组分。所提方法假设粒子代表波，其行为被随机建模。由于采用平滑先验设置，该推断在数据噪声较大时依然准确。此外，本文通过两组实验展示了该方法的可行性。
原文摘要 · Abstract (English)
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.
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