ResearchTrend.AI
  • Papers
  • Communities
  • Events
  • Blog
  • Pricing
Papers
Communities
Social Events
Terms and Conditions
Pricing
Parameter LabParameter LabTwitterGitHubLinkedInBlueskyYoutube

© 2025 ResearchTrend.AI, All rights reserved.

  1. Home
  2. Papers
  3. 2504.20504
14
0

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

29 April 2025
Yutong Du
Zicheng Liu
Miao Cao
Zupeng Liang
Yali Zong
Changyou Li
ArXivPDFHTML
Abstract

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.

View on arXiv
@article{du2025_2504.20504,
  title={ Quality-factor inspired deep neural network solver for solving inverse scattering problems },
  author={ Yutong Du and Zicheng Liu and Miao Cao and Zupeng Liang and Yali Zong and Changyou Li },
  journal={arXiv preprint arXiv:2504.20504},
  year={ 2025 }
}
Comments on this paper