Learned Patch-Based Regularization for Inverse Problems in Imaging

Many modern approaches to image reconstruction are based on learning a regularizer that implicitly encodes a prior over the space of images. For large-scale images common in imaging domains like remote sensing, medical imaging, astronomy, and others, learning the entire image prior requires an often-impractical amount of training data. This work describes a deep image patch-based regularization approach that can be incorporated into a variety of modern algorithms. Learning a regularizer amounts to learning the a prior for image patches, greatly reducing the dimension of the space to be learned and hence the sample complexity. Demonstrations in a remote sensing application illustrates that learning patch-based regularizers produces high-quality reconstructions and even permits learning from a single ground-truth image.