Efficient and accurate inversion of multiple scattering with deep learning
This addresses the challenge of efficient and accurate image reconstruction in applications like diffraction tomography, offering a novel deep learning solution.
The paper tackled the problem of image reconstruction under multiple light scattering by proposing a deep convolutional neural network as an alternative to optimization-based methods, resulting in substantially faster and higher-quality imaging compared to state-of-the-art approaches.
Image reconstruction under multiple light scattering is crucial in a number of applications such as diffraction tomography. The reconstruction problem is often formulated as a nonconvex optimization, where a nonlinear measurement model is used to account for multiple scattering and regularization is used to enforce prior constraints on the object. In this paper, we propose a powerful alternative to this optimization-based view of image reconstruction by designing and training a deep convolutional neural network that can invert multiple scattered measurements to produce a high-quality image of the refractive index. Our results on both simulated and experimental datasets show that the proposed approach is substantially faster and achieves higher imaging quality compared to the state-of-the-art methods based on optimization.