  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.
