Jae-Kwang Lee et al. (Eds) : CCSEA, AIFU, DKMP, CLOUD, EMSA, SEA, SIPRO - 2017 pp. 139– 147, 2017. © CS & IT-CSCP 2017 DOI : 10.5121/csit.2017.70213       Aliaa Youssef 1 , Sameh Zarif 2 and Amr Ghoneim 1 1 Department of Computer Science, Helwan University, Helwan, Egypt 2 Department of Information Technology, Menofia University, Menofia, Egypt ABSTRACT The majority of applications requiring high resolution images to derive and analyze data accurately and easily. Image super resolution is playing an effective role in those applications. Image super resolution is the process of producing high resolution image from low resolution image. In this paper, we study various image super resolution techniques with respect to the quality of results and processing time. This comparative study introduces a comparison between four algorithms of single image super-resolution. For fair comparison, the compared algorithms are tested on the same dataset and same platform to show the major advantages of one over the others. KEYWORDS Super resolution, Interpolation, Neighbour filling, Resizing, Low resolution 1. INTRODUCTION Supper resolution (SR) is the process of enhancement the resolution of images. Resolution is a measure of frequency content in an image. There are always requests for good quality images from low one, although the cameras for high resolution (HR) images are expensive. Also image capturing setting is not ideal so the resulting images are blurred and noisy. Regarding that using of supper resolution techniques to enhance resolution of images and maintain the details of them is preferable [1-4]. HR images are frequently used in large applications such as satellite imaging, sports images, medical imaging, computer vision, remote sensing, surveillance systems, object detection and recognition. The need of zooming of images to analyze visual information also increases the request for super-resolution [5-7]. In general, super resolution techniques are divided into two categories, which are multi image super-resolution and single image super-resolution [2]. Multi image super-resolution is the process of generation high resolution image from multiple low resolution images. Single image super-resolution is the process of generating high-resolution image from its low resolution image [8]. This study focuses on single image super-resolution techniques.