Image Super-Resolution Techniques based on Machine Learning: Comparative Review

The task of providing super-resolution is a task, which is mainly formulated in the inverse form and is solved by the method or a set of methods for preserving the finest details of an image by processing one input image or a set of input images of one scene. The image super-resolution is provided due to an increase in the number pixels per unit area in the original sample. Similar methods for the case of one input image are based on reconstruction or learning. Other methods, in the case of a set of images of one scene, use additional normatively based on the sub pixel offset on the non-target pixel value in the middle of the set. The purpose of the work is to study and compare single-frame image super-resolution techniques.