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Data-Decoupled Scattering Imaging Method Based on Autocorrelation Enhancement

Authors :
Chen Wang
Jiayan Zhuang
Sichao Ye
Wei Liu
Yaoyao Yuan
Hongman Zhang
Jiangjian Xiao
Source :
Applied Sciences, Vol 13, Iss 4, p 2394 (2023)
Publication Year :
2023
Publisher :
MDPI AG, 2023.

Abstract

Target recovery through scattering media is an important aspect of optical imaging. Although various algorithms combining deep-learning methods for target recovery through scattering media exist, they have limitations in terms of robustness and generalization. To address these issues, this study proposes a data-decoupled scattering imaging method based on autocorrelation enhancement. This method constructs basic-element datasets, acquires the speckle images corresponding to these elements, and trains a deep-learning model using the autocorrelation images generated from the elements using speckle autocorrelation as prior physical knowledge to achieve the scattering recovery imaging of targets across data domains. To remove noise terms and enhance the signal-to-noise ratio, a deep-learning model based on the encoder–decoder structure was used to recover a speckle autocorrelation image with a high signal-to-noise ratio. Finally, clarity reconstruction of the target is achieved by applying the traditional phase-recovery algorithm. The results demonstrate that this process improves the peak signal-to-noise ratio of the data from 15 to 37.28 dB and the structural similarity from 0.38 to 0.99, allowing a clear target image to be reconstructed. Meanwhile, supplementary experiments on the robustness and generalization of the method were conducted, and the results prove that it performs well on frosted glass plates with different scattering characteristics.

Details

Language :
English
ISSN :
13042394 and 20763417
Volume :
13
Issue :
4
Database :
Directory of Open Access Journals
Journal :
Applied Sciences
Publication Type :
Academic Journal
Accession number :
edsdoj.8f4e6dee0ae842c1bdce27c5148594d5
Document Type :
article
Full Text :
https://doi.org/10.3390/app13042394