Deep speckle correlation: a deep learning approach toward scalable imaging through scattering media

By Yunzhe Li, Yujia Xue, Lei Tian. Optica, 2018

434 citations recorded by the source corpus.

Imaging through scattering is an important yet challenging problem. Tremendous progress has been made by exploiting the deterministic input–output “transmission matrix” for a fixed medium. However, this “one-to-one” mapping is highly susceptible to speckle decorrelations – small perturbations to the scattering medium lead to model errors and severe degradation of the imaging performance. Our goal here is to develop a new framework that is highly scalable to both medium perturbations and measurement requirement. To do so, we propose a statistical “one-to-all” deep learning (DL) technique that encapsulates a wide range of statistical variations for the model to be resilient to speckle decorrelations. Specifically, we develop a convolutional neural network (CNN) that is able to learn the statistical information contained in the speckle intensity patterns captured on a set of diffusers having the same macroscopic parameter. We then show for the first time, to the best of our knowledge, that the trained CNN is able to generalize and make high-quality object predictions through an entirely different set of diffusers of the same class. Our work paves the way to a highly scalable DL approach for imaging through scattering media.

Subfields: Acoustics and Ultrasonics, Computational Mechanics, Instrumentation

View Paper: https://www.semanticscholar.org/paper/b11cc18414ebe86956737254cc40ffb55afed1fb

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In Acoustics and Ultrasonics

In Computational Mechanics

Laura Waller

In Instrumentation

Citations in the corpus

Roadmap on wavefront shaping and deep imaging in complex media (2021, background): “To overcome this limitation, a different training strategy is proposed in [132].”