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Performance evaluation of multi-frame super-resolution algorithms Nelson, Kyle, Bhatti, Asim and Nahavandi, Saeid 2013, Performance evaluation of multi-frame super-resolution algorithms, in DICTA 2012 : International Conference on Digital Image Computing Techniques and Applications, IEEE, Piscataway, N.J., pp. 1-8. Title Performance evaluation of multi-frame super-resolution algorithms Author(s) Nelson, Kyle Bhatti, Asim Nahavandi, Saeid Conference name Digital Image Computing Techniques and Applications. Conference (2012 : Fremantle, W.A) Conference location Fremantle, Western Australia Conference dates 3-5 Dec. 2012 Title of proceedings DICTA 2012 : International Conference on Digital Image Computing Techniques and Applications Editor(s) [Unknown] Publication date 2013 Conference series Digital Image Computing Techniques and Applications Conference Start page 1 End page 8 Total pages 8 Publisher IEEE Place of publication Piscataway, N.J. Keyword(s) super-resolution multi-frame image enhancement image quality performance evaluation comparison Summary Multi-frame super-resolution algorithms aim to increase spatial resolution by fusing information from several low-resolution perspectives of a scene. While a wide array of super-resolution algorithms now exist, the comparative capability of these techniques in practical scenarios has not been adequately explored. In addition, a standard quantitative method for assessing the relative merit of super-resolution algorithms is required. This paper presents a comprehensive practical comparison of existing super-resolution techniques using a shared platform and 4 common greyscale reference images. In total, 13 different super-resolution algorithms are evaluated, and as accurate alignment is critical to the super-resolution process, 6 registration algorithms are also included in the analysis. Pixel-based visual information fidelity (VIFP) is selected from the 12 image quality metrics reviewed as the measure most suited to the appraisal of super-resolved images. Experimental results show that Bayesian super-resolution methods utilizing the simultaneous autoregressive (SAR) prior produce the highest quality images when combined with generalized stochastic Lucas-Kanade optical flow registration. ISBN 9781467321815 9781467321792 Language eng Field of Research 080106 Image Processing Socio Economic Objective 970108 Expanding Knowledge in the Information and Computing Sciences HERDC Research category E1 Full written paper - refereed Copyright notice ©2013, IEEE Persistent URL http://hdl.handle.net/10536/DRO/DU:30050976 Document type: Conference Paper Collection: Centre for Intelligent Systems Research Related Links Link Description Connect to published version (restricted access) Go to link with your DU access privileges     Unless expressly stated otherwise, the copyright for items in DRO is owned by the author, with all rights reserved. Versions Version Filter Type Tue, 05 Mar 2013, 14:54:22 EST Fri, 19 Apr 2013, 13:19:45 EST Mon, 22 Apr 2013, 08:50:43 EST Wed, 24 Apr 2013, 08:28:14 EST Tue, 30 Apr 2013, 11:12:17 EST Filtered Full Access Statistics: 55 Abstract Views, 1 File Downloads  -  Detailed Statistics Created: Tue, 05 Mar 2013, 14:54:22 EST

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