Deep learning-assisted imaging through stationary scattering media

TL;DR

This paper demonstrates that deep learning, specifically GANs, can effectively mitigate stationary scattering effects in in-line optical imaging, enabling clearer object reconstruction and precise localization with minimal training data.

Contribution

The study introduces a deep learning approach using GANs to negate stationary scattering effects in in-line imaging, achieving rapid training and accurate object reconstruction with small datasets.

Findings

Conditional GANs can be trained with minimal data.

The method enables clear object reconstruction through scattering media.

It transforms shift-variant systems into shift-invariant ones for high-precision localization.

Abstract

Imaging through scattering media is a challenging problem owing to speckle decorrelations from perturbations in the media itself. For in-line imaging modalities, which are appealing because they are compact, require no moving parts, and are robust, negating the effects of such scattering becomes particularly challenging. Here we explore the effect of stationary scattering media on light scattering in in-line geometries, including digital holographic microscopy. We consider various object-scatterer scenarios where the object is distorted or obscured by additional stationary scatterers, and use an advanced deep learning (DL) generative methodology, generative adversarial networks (GANs), to mitigate the effects of the additional scatterers. Using light scattering simulations and experiments on objects of interest with and without additional scatterers, we find that conditional GANs can be quickly trained with minuscule datasets and can also efficiently learn the one-to-one statistical mapping between the cross-domain input-output image pairs. Training such a network yields a standalone model, that can be used later to inverse or negate the effect of scattering, yielding clear object reconstructions for object retrieval and downstream processing. Moreover, it is well-known that the coherent point spread function (c-PSF) of a stationary scattering optical system is a speckle pattern which is spatially shift variant. We show that with rapid training using only 20 image pairs, it is possible to negate this undesired scattering to accurately localize diffraction-limited impulses with high spatial accuracy, therefore transforming the earlier shift variant system to a linear shift invariant (LSI) system.