# Quality-factor inspired deep neural network solver for solving inverse scattering problems

arXiv: https://arxiv.org/abs/2504.20504 · HTML: https://mlsanity.com/paper/2504.20504/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 53 references that are in the map.
- First posted on arXiv 29 Apr 2025.

## Builds on (most cited first, 6 of 6)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [Squeeze-and-Excitation Networks](https://mlsanity.com/paper/1709.01507/) (2017)
- [DeepNIS: Deep Neural Network for Nonlinear Electromagnetic Inverse Scattering](https://mlsanity.com/paper/1810.03990/) (2018)
- [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)

## Built on by (most cited first, 5 of 5)

- [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)
- [Contrast-Source-Based Physics-Driven Neural Network for Inverse Scattering Problems](https://mlsanity.com/paper/2601.19243/) (2026)
- [Coordinate-Residual Physics-Driven Neural Network for Inverse Scattering Imaging](https://mlsanity.com/paper/2608.09382/) (2026)
- [Structured Multi-Level-Set Inversion for 3-D Electromagnetic Inverse Scattering](https://mlsanity.com/paper/2609.08594/) (2026)

## Abstract (arXiv)

Deep neural networks have been applied to address electromagnetic inverse scattering problems (ISPs) and shown superior imaging performances, which can be affected by the training dataset, the network architecture and the applied loss function. Here, the quality of data samples is cared and valued by the defined quality factor. Based on the quality factor, the composition of the training dataset is optimized. The network architecture is integrated with the residual connections and channel attention mechanism to improve feature extraction. A loss function that incorporates data-fitting error, physical-information constraints and the desired feature of the solution is designed and analyzed to suppress the background artifacts and improve the reconstruction accuracy. Various numerical analysis are performed to demonstrate the superiority of the proposed quality-factor inspired deep neural network (QuaDNN) solver and the imaging performance is finally verified by experimental imaging test.