Machine learning methods for computational imaging require uncertainty
estimation to be reliable in real settings. While Bayesian models offer a
computationally tractable way of recovering uncertainty, they need large data
volumes to be trained, which in imaging applications implicates prohibitively
expensive collections with specific imaging instruments. This paper introduces
a novel framework to train variational inference for inverse problems
exploiting in combination few experimentally collected data, domain expertise
and existing image data sets. In such a way, Bayesian machine learning models
can solve imaging inverse problems with minimal data collection efforts.
Extensive simulated experiments show the advantages of the proposed framework.
The approach is then applied to two real experimental optics settings:
holographic image reconstruction and imaging through highly scattering media.
In both settings, state of the art reconstructions are achieved with little
collection of training data.

本文介绍了一种新的框架，基于少量实验数据、领域专业知识和现有图像数据集来训练变分推断，使贝叶斯机器学习模型可以在最小数据收集效果下解决成像反问题。经过广泛的模拟实验证明了该方法的优点，并在两个实验光学设置中应用：全息图像重建和通过高度散射介质成像。在两种设置中，都用很少的训练数据，达到了最先进的重建效果。