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, doi: 10.1109/DICTA.2012.6411669.

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Title Performance evaluation of multi-frame super-resolution algorithms
Author(s) Nelson, KyleORCID iD for Nelson, Kyle
Bhatti, AsimORCID iD for Bhatti, Asim
Nahavandi, SaeidORCID iD for 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
image enhancement
image quality
performance evaluation
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 1467321818
Language eng
DOI 10.1109/DICTA.2012.6411669
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
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