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Journal of Biomedical Optics

Comparison of super-resolution algorithms applied to retinal images
Author(s): Damber Thapa; Kaamran Raahemifar; William R. Bobier; Vasudevan Lakshminarayanan
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Paper Abstract

A critical challenge in biomedical imaging is to optimally balance the trade-off among image resolution, signal-to-noise ratio, and acquisition time. Acquiring a high-resolution image is possible; however, it is either expensive or time consuming or both. Resolution is also limited by the physical properties of the imaging device, such as the nature and size of the input source radiation and the optics of the device. Super-resolution (SR), which is an off-line approach for improving the resolution of an image, is free of these trade-offs. Several methodologies, such as interpolation, frequency domain, regularization, and learning-based approaches, have been developed over the past several years for SR of natural images. We review some of these methods and demonstrate the positive impact expected from SR of retinal images and investigate the performance of various SR techniques. We use a fundus image as an example for simulations.

Paper Details

Date Published: 1 May 2014
PDF: 16 pages
J. Biomed. Opt. 19(5) 056002 doi: 10.1117/1.JBO.19.5.056002
Published in: Journal of Biomedical Optics Volume 19, Issue 5
Show Author Affiliations
Damber Thapa, Univ. of Waterloo (Canada)
Kaamran Raahemifar, Ryerson Univ. (Canada)
William R. Bobier, Univ. of Waterloo (Canada)
Vasudevan Lakshminarayanan, Univ. of Waterloo (Canada)
Univ. of Michigan (United States)

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