Paper Title
Image Super Resolution via External Learning based Techniques: A Review

Abstract
High-quality images have a significant and essential role in many applications such as remote sensing, aerial imaging, military fields, medical diagnosis, object tracking, video surveillance, and criminal justice. However, high-resolution imaging may not always be feasible due to limitations of the sensors and optics manufacturing technology, and it is proven to be very costly. Super image resolution is an approach used to generate a higher resolution image from lower resolution image(s) by employing image processing algorithms that are relatively inexpensive. In this paper, a survey on the single image super-resolution methods which are based on external database learning is provided. Keywords - Super Resolution, External-based Learning, Deep Learning, Sparse Coding, Anchored Regression, Regression Trees.