A Study on Holographic Particle Imaging Based on Fourier Imaging Networks

doi:10.7517/issn.1674-0475.231026

Abstract

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.

Key words: digital holography, deep learning, Fourier transform, particle localization

LI Yujie, CHENG Weizhe, YU Le, WANG Huaying, WANG Dan, LI Xueyan, LI Boshi. A Study on Holographic Particle Imaging Based on Fourier Imaging Networks[J]. IMAGING SCIENCE AND PHOTOCHEMISTRY, 2024, 42(1): 1-8.

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