Comparison of super-resolution algorithms applied to retinal images.
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.
University of Waterloo, School of Optometry and Vision Science, Waterloo, Ontario N2L 3G1, Canada.
Damber
D
Thapa
Ryerson University, Department of Electrical and Computer Engineering, Toronto, Ontario M5B 2K3, Canada.
Kaamran
K
Raahemifar
University of Waterloo, School of Optometry and Vision Science, Waterloo, Ontario N2L 3G1, Canada.
William R
WR
Bobier
University of Waterloo, School of Optometry and Vision Science, Waterloo, Ontario N2L 3G1, CanadacUniversity of Waterloo, Departments of Physics and Electrical and Computer Engineering, Waterloo, Ontario N2L 3G1, CanadadUniversity of Michigan, Department.
Vasudevan
V
Lakshminarayanan
D000465:Algorithms; D001185:Artificial Intelligence; D001499:Bayes Theorem; D003941:Diagnostic Techniques, Ophthalmological; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D012160:Retina / Q000033:anatomy & histology*; D012171:Retinal Vessels / Q000033:anatomy & histology*; D059629:Signal-To-Noise Ratio