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Speckle autocorrelation separation for multi-target scattering imaging

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posted on 2023-01-23, 13:53 authored by da lu, Feng Yuliu, Xiang Peng, Wenqi He
Imaging through scattering media remains a big challenge in optics while the single-shot non-invasive speckle autocorrelation technique (SAT) is well-known as a promising way to handle it. However, it usually cannot recover a large-scale target or multiple isolated small ones due to the limited effective range of the optical memory effect (OME). In this paper, we propose a multi-target scattering imaging scheme by combining the traditional SA algorithm with a Deep Learning (DL) strategy. The basic idea is to extract each autocorrelation component of every target from the autocorrelation result of a mixed speckle using a suitable DL method. Once we get all the expected autocorrelation components, a typical phase retrieval algorithm (PRA) could be applied to reveal the shapes of all those corresponding small targets. In our experimental demonstration, up to five isolated targets are successfully recovered.


Funder Name

National Natural Science Foundation of China (62061136005,61875129,61805152); Sino-German Center for Research Promotion (GZ 1391,M-0044); Natural Science Foundation of Guangdong Province (2021A1515011801)

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