Learning-based imaging through scattering media | Zendy

Ryoichi Horisaki | Zendy; Ryosuke Takagi | Zendy; Jun Tanida | Zendy

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Learning-based imaging through scattering media

Author(s) -

Ryoichi Horisaki,

Ryosuke Takagi,

Jun Tanida

Publication year - 2016

Publication title -

optics express

Language(s) - Uncategorized

Resource type - Journals

SCImago Journal Rank - 1.394

H-Index - 271

ISSN - 1094-4087

DOI - 10.1364/oe.24.013738

Subject(s) - scattering , optics , inverse scattering problem , speckle pattern , light scattering , x ray scattering techniques , computer science , phase retrieval , speckle noise , speckle imaging , inverse problem , artificial intelligence , physics , x ray raman scattering , mathematics , inelastic scattering , mathematical analysis , fourier transform , quantum mechanics

We present a machine-learning-based method for single-shot imaging through scattering media. The inverse scattering process was calculated based on a nonlinear regression algorithm by learning a number of training object-speckle pairs. In the experimental demonstration, multilayer phase objects between scattering plates were reconstructed from intensity measurements. Our approach enables model-free sensing, where it is not necessary to know the sensing processes/models.

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