影像科学与光化学2024，Vol.42Issue(1)：1-8,8.DOI:10.7517/issn.1674-0475.231026
基于傅里叶成像网络的全息粒子成像技术研究
A Study on Holographic Particle Imaging Based on Fourier Imaging Networks
摘要
Abstract
Digital holographic reconstruction can obtain three-dimensional information of objects,and it has significant scientific significance and practical value in medical,environmental protection,chemical engineering,and other fields that require measurement of particle size and three-dimensional information.The particle holographic localization often faces issues of computational complexity and insufficient reconstruction accuracy.To address these issues,we adopted a method based on the Fourier imaging network.By leveraging the characteristics of the Fourier transform,we were able to effectively extract frequency features from the hologram.Combined with deep learning technology,the network utilized learnable filters and a global receptive field to process the spatial frequencies of input data.This approach not only enhanced data processing efficiency but also endowed the system with superior feature extraction capabilities,thereby achieving precise particle localization.In this paper,we validated our approach through both experimental and simulation studies and compared it with the Dense_U_net network.The results show that the method based on the Fourier Imaging Network significantly improved both localization accuracy and speed.关键词
数字全息/深度学习/傅里叶变换/粒子定位Key words
digital holography/deep learning/Fourier transform/particle localization引用本文复制引用
李宇杰,程伟哲,余乐,王华英,王丹,李学燕,李伯仕..基于傅里叶成像网络的全息粒子成像技术研究[J].影像科学与光化学,2024,42(1):1-8,8.基金项目
深圳市科技计划项目资助(JCYJ20220530164808019,JCYJ20230807113559010) （JCYJ20220530164808019,JCYJ20230807113559010）
