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Image reconstruction under multiple light scattering is crucial in a numberof applications such as diffraction tomography. The reconstruction problem isoften formulated as a nonconvex optimization, where a nonlinear measurementmodel is used to account for multiple scattering and regularization is used toenforce prior constraints on the object. In this paper, we propose a powerfulalternative to this optimization-based view of image reconstruction bydesigning and training a deep convolutional neural network that can invertmultiple scattered measurements to produce a high-quality image of therefractive index. Our results on both simulated and experimental datasets showthat the proposed approach is substantially faster and achieves higher imagingquality compared to the state-of-the-art methods based on optimization.

Efficient and accurate inversion of multiple scattering with deep learning