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

TL;DR

This paper introduces a deep learning framework that improves imaging through scattering media by capturing statistical variations, making it resilient to medium perturbations and reducing measurement requirements.

Contribution

It proposes a statistical 'one-to-all' CNN approach that generalizes across different diffusers, enhancing scalability and robustness in scattering media imaging.

Findings

CNN learns statistical features of speckle patterns

Model generalizes to different diffusers of the same class

Enables high-quality imaging despite medium perturbations

Abstract

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 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 deep learning approach for imaging through scattering media.