Towards machine learning for heterogeneous inverse scattering in 3D microscopy.

Light propagating through a nonuniform medium scatters as it interacts with particles with different refractive properties such as cells in the tissue. In this work we aim to utilize this scattering process to learn a volumetric reconstruction of scattering parameters, in particular particle densities. We target microscopy applications where coherent speckle effects are an integral part of the imaging process. We argue that the key for successful learning is modeling realistic speckles in the training process. To this end, we build on the development of recent physically accurate speckle simulators. We also explore how to incorporate speckle statistics, such as the memory effect, in the learning framework. Overall, this paper contributes an analysis of multiple aspects of the network design including the learning architecture, the training data and the desired input features. We hope this study will pave the road for future design of learning based imaging systems in this challenging domain.