  Deep learning has been broadly applied to imaging in scattering applications.
A common framework is to train a descattering network for image recovery by
removing scattering artifacts. To achieve the best results on a broad spectrum
of scattering conditions, individual "expert" networks need to be trained for
each condition. However, the expert's performance sharply degrades when the
testing condition differs from the training. An alternative brute-force
approach is to train a "generalist" network using data from diverse scattering
conditions. It generally requires a larger network to encapsulate the diversity
in the data and a sufficiently large training set to avoid overfitting. Here,
we propose an adaptive learning framework, termed dynamic synthesis network
(DSN), which dynamically adjusts the model weights and adapts to different
scattering conditions. The adaptability is achieved by a novel "mixture of
experts" architecture that enables dynamically synthesizing a network by
blending multiple experts using a gating network. We demonstrate the DSN in
holographic 3D particle imaging for a variety of scattering conditions. We show
in simulation that our DSN provides generalization across a continuum of
scattering conditions. In addition, we show that by training the DSN entirely
on simulated data, the network can generalize to experiments and achieve robust
3D descattering. We expect the same concept can find many other applications,
such as denoising and imaging in scattering media. Broadly, our dynamic
synthesis framework opens up a new paradigm for designing highly adaptive deep
learning and computational imaging techniques.
