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Speckle autocorrelation separation for multi-target scattering imaging
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OA
AI
DOI：10.1364/OE.479943.png)
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Abstract
En 中文
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.
Keywords:
MEMORY EFFECT RANGE
TRANSMISSION MATRIX
MEDIA
LAYERS
RECONSTRUCTION
CORNERS
RETRIEVAL
WAVES
TIME