# Efficient and accurate inversion of multiple scattering with deep learning

arXiv: https://arxiv.org/abs/1803.06594 · HTML: https://mlsanity.com/paper/1803.06594/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 18 Mar 2018.

## Built on by (most cited first, 10 of 10)

- [InverseBench: Benchmarking Plug-and-Play Diffusion Priors for Inverse Problems in Physical Sciences](https://mlsanity.com/paper/2503.11043/) (2025)
- [Recovery of Continuous 3D Refractive Index Maps from Discrete Intensity-Only Measurements using Neural Fields](https://mlsanity.com/paper/2112.00002/) (2021)
- [Online Deep Equilibrium Learning for Regularization by Denoising](https://mlsanity.com/paper/2205.13051/) (2022)
- [SGD-Net: Efficient Model-Based Deep Learning with Theoretical Guarantees](https://mlsanity.com/paper/2101.09379/) (2021)
- [WaveMo: Learning Wavefront Modulations to See Through Scattering](https://mlsanity.com/paper/2404.07985/) (2024)
- [Revisit Geophysical Imaging in A New View of Physics-informed Generative Adversarial Learning](https://mlsanity.com/paper/2109.11452/) (2021)
- [Neural Inverse Scattering with Score-based Regularization](https://mlsanity.com/paper/2505.14560/) (2025)
- [Theory-guided Pseudo-spectral Full Waveform Inversion via Deep Neural Networks](https://mlsanity.com/paper/2502.17624/) (2025)
- [A Model-Guided Neural Network Method for the Inverse Scattering Problem](https://mlsanity.com/paper/2512.10123/) (2025)
- [ScoreField: Neural Inverse Scattering with Score-Based Generative Priors](https://mlsanity.com/paper/2608.02937/) (2026)

## Abstract (arXiv)

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.