  Speckle noise has long been an extensively studied problem in medical
imaging. In recent years, there have been significant advances in leveraging
deep learning methods for noise reduction. Nevertheless, adaptation of
supervised learning models to unseen domains remains a challenging problem.
Specifically, deep neural networks (DNNs) trained for computational imaging
tasks are vulnerable to changes in the acquisition system's physical
parameters, such as: sampling space, resolution, and contrast. Even within the
same acquisition system, performance degrades across datasets of different
biological tissues. In this work, we propose a few-shot supervised learning
framework for optical coherence tomography (OCT) noise reduction, that offers a
dramatic increase in training speed and requires only a single image, or part
of an image, and a corresponding speckle suppressed ground truth, for training.
Furthermore, we formulate the domain shift problem for OCT diverse imaging
systems, and prove that the output resolution of a despeckling trained model is
determined by the source domain resolution. We also provide possible remedies.
We propose different practical implementations of our approach, verify and
compare their applicability, robustness, and computational efficiency. Our
results demonstrate significant potential for generally improving sample
complexity, generalization, and time efficiency, for coherent and non-coherent
noise reduction via supervised learning models, that can also be leveraged for
other real-time computer vision applications.
