A Survey: The Methods & Techniques of Super-Resolution Image Reconstruction

Super-resolution is the process of recovering a high-resolution image from multiple low-resolution images of the same scene. The key objective of super-resolution (SR) imaging is to reconstruct a higher-resolution image based on a set of images, acquired from the same scene and denoted as ‘low-resolution’ images, to overcome the limitation and/or ill-posed conditions of the image acquisition process for facilitating better content visualization and scene recognition. In this paper, we provide a comprehensive review of existing super-resolution techniques and highlight the future research challenges. This includes the formulation of an observation model and coverage of the dominant algorithm – Iterative back projection .We critique these methods and identify areas which promise performance improvements. In this paper, future directions for super-resolution algorithms are discussed. Finally results of available methods are given.