SINGLE IMAGE SUPER RESOLUTION: A COMPARATIVE STUDY (original) (raw)

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.

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  25. AUTHORS Sameh Zarif received his BSc and MSc degrees in information technology from Menofia University, Egypt, in 2005 and 2009 respectively. He completed his Doctor of Philosophy from centre of intelligent signal & imaging research (CISIR), Universiti Teknologi PETRONAS (UTP), Malaysia 2015. Currently he is an assistant Professor in Department of Information Technology at Menofia University Egypt. In addition to his current research into image super resolution, texture synthesis, and image completion, his interests lie in image processing, computer vision, and pattern recognition.