  Image restoration, or inverse problems in image processing, has long been an
extensively studied topic. In recent years supervised learning approaches have
become a popular strategy attempting to tackle this task. Unfortunately, most
supervised learning-based methods are highly demanding in terms of
computational resources and training data (sample complexity). In addition,
trained models are sensitive to domain changes, such as varying acquisition
systems, signal sampling rates, resolution and contrast. In this work, we try
to answer a fundamental question: Can supervised learning models generalize
well solely by learning from one image or even part of an image? If so, then
what is the minimal amount of patches required to achieve acceptable
generalization? To this end, we focus on an efficient patch-based learning
framework that requires a single image input-output pair for training.
Experimental results demonstrate the applicability, robustness and
computational efficiency of the proposed approach for supervised image
deblurring and super-resolution. Our results showcase significant improvement
of learning models' sample efficiency, generalization and time complexity, that
can hopefully be leveraged for future real-time applications, and applied to
other signals and modalities.
