Learning from small data sets: Patch-based regularizers in inverse
  problems for image reconstruction
Moritz Piening Fabian Altekrüger Johannes Hertrich Paul Hagemann Andrea Walther Gabriele Steidl
The solution of inverse problems is of fundamental interest in medical and
astronomical imaging, geophysics as well as engineering and life sciences.
Recent advances were made by using methods from machine learning, in particular
deep neural networks. Most of these methods require a huge amount of (paired)
data and computer capacity to train the networks, which often may not be
available. Our paper addresses the issue of learning from small data sets by
taking patches of very few images into account. We focus on the combination of
model-based and data-driven methods by approximating just the image prior, also
known as regularizer in the variational model. We review two methodically
different approaches, namely optimizing the maximum log-likelihood of the patch
distribution, and penalizing Wasserstein-like discrepancies of whole empirical
patch distributions. From the point of view of Bayesian inverse problems, we
show how we can achieve uncertainty quantification by approximating the
posterior using Langevin Monte Carlo methods. We demonstrate the power of the
methods in computed tomography, image super-resolution, and inpainting. Indeed,
the approach provides also high-quality results in zero-shot super-resolution,
where only a low-resolution image is available. The paper is accompanied by a
GitHub repository containing implementations of all methods as well as data
examples so that the reader can get their own insight into the performance.