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• W3117976811 abstract "Scattering medium brings great difficulties to locate and reconstruct objects especially when the objects are distributed in different positions. In this paper, a novel physics and learning-heuristic method is presented to locate and image the object through a strong scattering medium. A novel physics-informed framework, named DINet, is constructed to predict the depth and the image of the hidden object from the captured speckle pattern. With the phase-space constraint and the efficient network structure, the proposed method enables to locate the object with a depth mean error less than 0.05 mm, and image the object with an average peak signal-to-noise ratio (PSNR) above 24 dB, ranging from 350 mm to 1150 mm. The constructed DINet firstly solves the problem of quantitative locating and imaging via a single speckle pattern in a large depth. Comparing with the traditional methods, it paves the way to the practical applications requiring multi-physics through scattering media." @default.
• W3117976811 created "2021-01-05" @default.
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• W3117976811 date "2020-12-25" @default.
• W3117976811 modified "2023-10-18" @default.
• W3117976811 title "Locating and Imaging through Scattering Medium in a Large Depth" @default.
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• W3117976811 doi "https://doi.org/10.3390/s21010090" @default.
• W3117976811 hasPubMedCentralId "https://www.ncbi.nlm.nih.gov/pmc/articles/7796240" @default.
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