Unrolled Convolutional Neural Network for Full-Wave Inverse Scattering
Author:
Affiliation:
1. Laboratoire de Génie Electrique et Electronique de Paris, CentraleSupélec, CNRS, Université Paris-Saclay, Gif-sur-Yvette, France
2. Laboratoire des Signaux et Systèmes, CNRS, CentraleSupélec, Université Paris-Saclay, Gif-sur-Yvette, France
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Subject
Electrical and Electronic Engineering
Link
http://xplorestaging.ieee.org/ielx7/8/10021137/09933180.pdf?arnumber=9933180
Reference40 articles.
1. PyTorch: An imperative style, high-performance deep learning library;paszke;Advances in Neural Information Processing System,2019
2. Adam: A method for stochastic optimization;kingma;Proc Int Conf Learn Represent,2015
3. ISTA-Net: Interpretable Optimization-Inspired Deep Network for Image Compressive Sensing
4. Unrolled optimization with deep priors;diamond;arXiv 1705 08041,2017
5. Neural proximal gradient descent for compressive imaging;mardani;Advances in neural information processing systems,2018
Cited by 12 articles. 订阅此论文施引文献 订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献
1. A residual fully convolutional network (Res-FCN) for electromagnetic inversion of high contrast scatterers at an arbitrary frequency within a wide frequency band;Inverse Problems;2024-05-07
2. Application of a mild data-driven technique to Lippmann–Schwinger inverse scattering in variable-exponent Lebesgue spaces for microwave imaging;Inverse Problems;2024-05-03
3. Deep-Learning-Based Source Reconstruction Method Using Deep Convolutional Conditional Generative Adversarial Network;IEEE Transactions on Microwave Theory and Techniques;2024-05
4. Contrast source inversion of sparse targets through multi-resolution Bayesian compressive sensing;Inverse Problems;2024-04-18
5. A Mild Data-Driven Approach Based on a Lebesgue-Space Inversion Procedure for Microwave Imaging Applications;2024 18th European Conference on Antennas and Propagation (EuCAP);2024-03-17
1.学者识别学者识别
2.学术分析学术分析
3.人才评估人才评估
"同舟云学术"是以全球学者为主线，采集、加工和组织学术论文而形成的新型学术文献查询和分析系统，可以对全球学者进行文献检索和人才价值评估。用户可以通过关注某些学科领域的顶尖人物而持续追踪该领域的学科进展和研究前沿。经过近期的数据扩容，当前同舟云学术共收录了国内外主流学术期刊6万余种，收集的期刊论文及会议论文总量共计约1.5亿篇，并以每天添加12000余篇中外论文的速度递增。我们也可以为用户提供个性化、定制化的学者数据。欢迎来电咨询！咨询电话：010-8811{复制后删除}0370
www.globalauthorid.com
TOP
Copyright © 2019-2024 北京同舟云网络信息技术有限公司 京公网安备11010802033243号 京ICP备18003416号-3