Data Augmentation for Multi-Image Super-Resolution

Abstract

Super-resolution reconstruction consists in generating a high-resolution image from a single low-resolution image or multiple images presenting the same area of interest. Existing state-of-the-art approaches to single-image and multi-image super-resolution are based on deep learning that requires large amounts of training data. They are commonly obtained by simulating low-resolution images from an original image treated as a high-resolution reference, but such simulation may not reflect the real-life operating conditions. Therefore, a serious obstacle in deploying super-resolution in real-world cases results from the lack of training data that would encompass real low-resolution images coupled with a real high-resolution reference. In this paper, we propose a new data augmentation technique underpinned with learning the relation between high and low resolution. This helps reduce the requirements concerned with the amount of real-life data necessary to train a super-resolution network, while providing higher-quality data for training, compared with the simulated low-resolution images. Our initial experimental results reported in the paper confirm that the proposed approach is suitable for multi-image super-resolution.