# DeepNIS: Deep Neural Network for Nonlinear Electromagnetic Inverse Scattering

arXiv: https://arxiv.org/abs/1810.03990 · HTML: https://mlsanity.com/paper/1810.03990/

## Key facts

- Cited by 31 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 4 Oct 2018.

## Built on by (most cited first, 30 of 31)

- [InverseBench: Benchmarking Plug-and-Play Diffusion Priors for Inverse Problems in Physical Sciences](https://mlsanity.com/paper/2503.11043/) (2025)
- [A Survey of Complex-Valued Neural Networks](https://mlsanity.com/paper/2101.12249/) (2021)
- [Physics-guided Loss Functions Improve Deep Learning Performance in Inverse Scattering](https://mlsanity.com/paper/2111.09109/) (2021)
- [Physics Embedded Machine Learning for Electromagnetic Data Imaging](https://mlsanity.com/paper/2207.12607/) (2022)
- [Neural Born Iteration Method For Solving Inverse Scattering Problems: 2D Cases](https://mlsanity.com/paper/2112.09831/) (2021)
- [Imaging Interiors: An Implicit Solution to Electromagnetic Inverse Scattering Problems](https://mlsanity.com/paper/2407.09352/) (2024)
- [Quality-factor inspired deep neural network solver for solving inverse scattering problems](https://mlsanity.com/paper/2504.20504/) (2025)
- [Physics-Driven Neural Network for Solving Electromagnetic Inverse Scattering Problems](https://mlsanity.com/paper/2507.16321/) (2025)
- [RayProNet: A Neural Point Field Framework for Radio Propagation Modeling in 3D Environments](https://mlsanity.com/paper/2406.16907/) (2024)
- [Deep Injective Prior for Inverse Scattering](https://mlsanity.com/paper/2301.03092/) (2023)
- [MITNet: GAN Enhanced Magnetic Induction Tomography Based on Complex CNN](https://mlsanity.com/paper/2102.07911/) (2021)
- [Real-Time Model-Based Quantitative Ultrasound and Radar](https://mlsanity.com/paper/2402.10520/) (2024)
- [Multi-frequency Neural Born Iterative Method for Solving 2-D Inverse Scattering Problems](https://mlsanity.com/paper/2409.01315/) (2024)
- [Plug-and-Play Latent Diffusion for Electromagnetic Inverse Scattering with Application to Brain Imaging](https://mlsanity.com/paper/2509.04860/) (2025)
- [Improved Physics-Driven Neural Network to Solve Inverse Scattering Problems](https://mlsanity.com/paper/2512.09333/) (2025)
- [Fast Physics-Driven Untrained Network for Highly Nonlinear Inverse Scattering Problems](https://mlsanity.com/paper/2602.13805/) (2026)
- [Graph Attention Network For Microwave Imaging of Brain Anomaly](https://mlsanity.com/paper/2108.01965/) (2021)
- [A Trained Regularization Approach Based on Born Iterative Method for Electromagnetic Imaging](https://mlsanity.com/paper/2112.13367/) (2021)
- [EcoFlow: Efficient Convolutional Dataflows for Low-Power Neural Network Accelerators](https://mlsanity.com/paper/2202.02310/) (2022)
- [A Differentiable Framework for Full and Phaseless Data Inversion Using Neural Implicit Contrast-Source Representation](https://mlsanity.com/paper/2508.10555/) (2025)
- [Introducing: DeepHead, Wide-band Electromagnetic Imaging Paradigm](https://mlsanity.com/paper/2107.11107/) (2021)
- [Towards Large-scale Single-shot Millimeter-wave Imaging for Low-cost Security Inspection](https://mlsanity.com/paper/2305.15750/) (2023)
- [GAN-driven Electromagnetic Imaging of 2-D Dielectric Scatterers](https://mlsanity.com/paper/2402.10831/) (2024)
- [3-D Magnetotelluric Deep Learning Inversion Guided by Pseudo-Physical Information](https://mlsanity.com/paper/2410.09388/) (2024)
- [Integrated Image Reconstruction and Target Recognition based on Deep Learning Technique](https://mlsanity.com/paper/2505.04836/) (2025)
- [Electromagnetic Inverse Scattering from a Single Transmitter](https://mlsanity.com/paper/2506.21349/) (2025)
- [Generalizable Holographic Reconstruction via Amplitude-Only Diffusion Priors](https://mlsanity.com/paper/2509.12728/) (2025)
- [Contrast-Source-Based Physics-Driven Neural Network for Inverse Scattering Problems](https://mlsanity.com/paper/2601.19243/) (2026)
- [ScoreField: Neural Inverse Scattering with Score-Based Generative Priors](https://mlsanity.com/paper/2608.02937/) (2026)
- [Coordinate-Residual Physics-Driven Neural Network for Inverse Scattering Imaging](https://mlsanity.com/paper/2608.09382/) (2026)

## Abstract (arXiv)

Nonlinear electromagnetic (EM) inverse scattering is a quantitative and super-resolution imaging technique, in which more realistic interactions between the internal structure of scene and EM wavefield are taken into account in the imaging procedure, in contrast to conventional tomography. However, it poses important challenges arising from its intrinsic strong nonlinearity, ill-posedness, and expensive computation costs. To tackle these difficulties, we, for the first time to our best knowledge, exploit a connection between the deep neural network (DNN) architecture and the iterative method of nonlinear EM inverse scattering. This enables the development of a novel DNN-based methodology for nonlinear EM inverse problems (termed here DeepNIS). The proposed DeepNIS consists of a cascade of multi-layer complexvalued residual convolutional neural network (CNN) modules. We numerically and experimentally demonstrate that the DeepNIS outperforms remarkably conventional nonlinear inverse scattering methods in terms of both the image quality and computational time. We show that DeepNIS can learn a general model approximating the underlying EM inverse scattering system. It is expected that the DeepNIS will serve as powerful tool in treating highly nonlinear EM inverse scattering problems over different frequency bands, involving large-scale and high-contrast objects, which are extremely hard and impractical to solve using conventional inverse scattering methods.