Upendra Bhatt et al, International Journal of Computer Science and Mobile Computing, Vol.5 Issue.6, June- 2016, pg. 495-499 © 2016, IJCSMC All Rights Reserved 495 Available Online at www.ijcsmc.com International Journal of Computer Science and Mobile Computing A Monthly Journal of Computer Science and Information Technology ISSN 2320–088X IMPACT FACTOR: 5.258 IJCSMC, Vol. 5, Issue. 6, June 2016, pg.495 – 499 A Review on Image Resolution Enhancement Methods in Spatial and Frequency Domain Upendra Bhatt 1 , Dr. Annapurna Singh 2 Computer Science and Engg., Department, GBPEC Ghurdauri Pauri Garhwal 1,2 Uttrakhand (246194)-India 1 Upendra.bhatt12@gmail.com; 2 Annapurnasingh78@gmail.com Abstract— In computer vision field, Image resolution enhancement has become the most current research area. Improving image resolution by applying costly hardware is expensive and time-consuming. In this paper we have discussed different methods to improve the resolution of the single image and we have given a comparison of different Super resolution algorithms for standard images based on PSNR and SSIM (Structure Similarity Index). Keywords— Resolution Enhancement, Super-resolution, Spatial domain, frequency domain, PSNR, SSIM I. INTRODUCTION Zooming a picture is ver essential in the field where someone wants to obtain more detailed information from an image. Low resolution(LR) images do not contain more information so to retrieve more information from that image Super-resolution(SR) is applied on LR images. The resolution enhancement work began in 1984 when Tsai and Huang [1] has introduced a mathematical model to obtain a single high-resolution image from a single or multiple LR images. This paper provides the review of the work done in the area of super-resolution in the spatial as well as in the frequency domain. This paper is organized into following sections. Section II describes spatial domain methods of super-resolution, section III gives method in the frequency domain, section IV contains comparison between some of the well-known algorithms and finally in the last section Conclusion is drawn. II. SUPER RESOLUTION METHODS IN SPATIAL DOMAIN 1. Nearest-Neighbor Interpolation When an image is interpolated new holes or pixels get generated and they need some value so that they can contain some information. In nearest-neighbor interpolation new interpolated pixel gets the pixel value of its neighbouring pixel.[2]