Semi-Simulated Training Data for Multi-Image Super-Resolution

Abstract

Multi-image super-resolution is a branch of super-resolution reconstruction techniques aiming to resolve a set of lowresolution images into a high-resolution one. Unlike singleimage super-resolution, its goal is to fuse information embedded in different images depicting the same scene, usually captured at different times. Such data combination contains more high-resolution information than a single image, thus allowing for more accurate reconstruction results. One of the most critical challenges is to prepare training data for multi-image super-resolution since only a few datasets are available here, especially for satellite imaging applications. For this reason, many studies are conducted using simulated low-resolution images, but the results obtained for real-life data are often unsatisfactory. To overcome this problem, we propose a new semi-simulated approach of creating lowresolution images for training that resemble real-life ones much more accurately. We also investigate the performance of selected deep learning models trained with simulated and semi-simulated datasets and we show that the latter achieve better results when applied to real-world images.