Comparison of super-resolution algorithms applied to retinal images
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Date
2014-05-01
Authors
Thapa, Damber
Raahemifar, Kaamran
Bobier, William
Lakshminarayanan, Vasudevan
Journal Title
Journal ISSN
Volume Title
Publisher
Society of Photo Optical Instrumentation Engineers
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.
Description
Keywords
reconstruction, super-resolution, retinal image analysis, sparse representation, inverse problem, example-based, ophthalmology