Performance Evaluation of Super-Resolution Reconstruction Algorithms Based On Linear Magnifications
Super Resolution based Reconstruction of images produces a High Resolution (HR) image from multiple Low Resolution (LR) images by estimating the motion parameters and shifts in the LR images. The problem can be divided into two parts: an image registration part in which the motion parameters and shift between different frames of the same scene are estimated and the reconstruction part in which an HR image is reconstructed from the registered images. In this paper, we consider the second part of the problem: The reconstruction step. We have compared six different reconstruction algorithms which are Bi-Cubic Interpolation method, Iterated Back Projection (IBP) algorithm, Points onto Convex Sets (POCS), Robust Super-Resolution (RSR), Structured-Adaptive Normalized Convolution (SANC) and Populis-Gerchberg (PG) approach. The results are compared using Histogram Comparison Index (HCI) based on BHATTACHARYYA [23] distance which is a popular metric for color images comparisons. From an experimental evaluation, we find that SANC, POCS and Bi-Cubic Interpolation methods produce convincing results both under high and low magnification compared to other methods. On the other hand, PG algorithm and RSR degrade image quality on higher magnification.
Keywords: Super-Resolution, Reconstruction Based Super-Resolution, Example Based Super-Resolution, Histogram Comparison Index, Image Registration
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ABOUT THE AUTHORS
Muhammad Siddique
Muhammad Siddique is pursuing his M.Sc Computer Systems Engineering from University of Engineering and Technology, Peshawar, Pakistan. He did his B.Sc. Computer Systems Engineering from the same university. He is working as a Computer Engineer in Ghulam Ishaq Khan Institute (GIKI) of Engineering Sciences and Technology, Topi, Pakistan. His research interest’s areas are image processing, computer vision, machine learning and pattern recognition.
Rehanullah Khan
Dr. Rehanullah graduated from University of Engineering and Technology Peshawar, with a B.Sc degree (Computer Engineering) in 2004 and M.Sc (Information Systems) in 2006. He obtained PhD degree (Computer Engineering) in 2011 from Vienna University of Technology, Austria. He is currently an Assistant Professor at Sarhad University of Science and Technology, Peshawar. His research interests include color interpretation, segmentation and object recognition.
Khalil Khan
Khalil Khan did B.Sc. Electrical Engineering from University of engineering and Technology, Peshawar, Pakistan. He is also pursuing his M.Sc. Computer System engineering from University of Engineering and Technology, Peshawar. He worked as switching engineer at ZTE Pvt. Ltd. in Optical fiber and Switching department for two and half year. Currently he is working as a Principal Dir College of Science and Technology.
Nasir Ahmad
Dr. Nasir Ahmad graduated from University of Engineering and Technology Peshawar with a B.Sc Electrical Engineering degree. He obtained his PhD degree from UK in 2011. He is a faculty member of Department of Computer Systems Engineering, University of Engineering and Technology Peshawar, Pakistan. His Research Areas include Pattern Recognition, and Digital Signal Processing.
Muhammad Siddique
Muhammad Siddique is pursuing his M.Sc Computer Systems Engineering from University of Engineering and Technology, Peshawar, Pakistan. He did his B.Sc. Computer Systems Engineering from the same university. He is working as a Computer Engineer in Ghulam Ishaq Khan Institute (GIKI) of Engineering Sciences and Technology, Topi, Pakistan. His research interest’s areas are image processing, computer vision, machine learning and pattern recognition.
Rehanullah Khan
Dr. Rehanullah graduated from University of Engineering and Technology Peshawar, with a B.Sc degree (Computer Engineering) in 2004 and M.Sc (Information Systems) in 2006. He obtained PhD degree (Computer Engineering) in 2011 from Vienna University of Technology, Austria. He is currently an Assistant Professor at Sarhad University of Science and Technology, Peshawar. His research interests include color interpretation, segmentation and object recognition.
Khalil Khan
Khalil Khan did B.Sc. Electrical Engineering from University of engineering and Technology, Peshawar, Pakistan. He is also pursuing his M.Sc. Computer System engineering from University of Engineering and Technology, Peshawar. He worked as switching engineer at ZTE Pvt. Ltd. in Optical fiber and Switching department for two and half year. Currently he is working as a Principal Dir College of Science and Technology.
Nasir Ahmad
Dr. Nasir Ahmad graduated from University of Engineering and Technology Peshawar with a B.Sc Electrical Engineering degree. He obtained his PhD degree from UK in 2011. He is a faculty member of Department of Computer Systems Engineering, University of Engineering and Technology Peshawar, Pakistan. His Research Areas include Pattern Recognition, and Digital Signal Processing.
