I. Introduction
High-Resolution (HR) images provide richer details of objects being observed and are preferred in various computer vision tasks such as detection and/or feature extraction, including human perception. The spatial resolution of the imaging sensor plays a crucial role in acquiring images with high resolution. While sensors with HR capability are preferred in most applications, several factors such as cost of production, space requirements of sensor, ease of manufacturing hinder HR sensors for broader application. Software-based solutions called image Super-Resolution (SR) is proposed to overcome this limitation to a certain extent. SR solutions are both economic and effective alternatives to demanding the use/replacement of HR sensors. The goal in the SR problem is to estimate HR images from a given Low-Resolution (LR) image or a set of LR images. Despite extensive SR works presented in the literature, the inherent ill-posed nature of the problem, complexity and unavailability of practical quantitative measurements make it an open research problem in the community [1].