Vol.125, No.1, 2020, pp.125-143, doi:10.32604/cmes.2020.011331
OPEN ACCESS
ARTICLE
Deep Residual Network Based on Image Priors for Single Image Super Resolution in FFA Images
  • G. R. Hemalakshmi*, D. Santhi, V. R. S. Mani, A. Geetha, N. B. Prakash
National Engineering College, Kovilpatti, 628503, India
* Corresponding Author: G. R. Hemalakshmi. Email: grhemalakshmi@gmail.com
Received 01 May 2020; Accepted 24 July 2020; Issue published 18 September 2020
Abstract
Diabetic retinopathy, aged macular degeneration, glaucoma etc. are widely prevalent ocular pathologies which are irreversible at advanced stages. Machine learning based automated detection of these pathologies facilitate timely clinical interventions, preventing adverse outcomes. Ophthalmologists screen these pathologies with fundus Fluorescein Angiography Images (FFA) which capture retinal components featuring diverse morphologies such as retinal vasculature, macula, optical disk etc. However, these images have low resolutions, hindering the accurate detection of ocular disorders. Construction of high resolution images from these images, by super resolution approaches expedites the diagnosis of pathologies with better accuracy. This paper presents a deep learning network for Single Image Super Resolution (SISR) of fundus fluorescein angiography images, modeled on residual learning, gridded interpolation and Swish activation functions. The image prior for this network is constructed by gridded interpolation which provides better image fidelity compared to other priors. Evaluation of the performance of this network and comparative analysis with benchmark architectures, on a standard dataset shows that the proposed network is superior with respect to performance metrics and computational time.
Keywords
SISR; FFA; residual network; gridded interpolation; swish function
Cite This Article
Hemalakshmi, G. R., Santhi,, D., Geetha, A., Prakash, N. B. (2020). Deep Residual Network Based on Image Priors for Single Image Super Resolution in FFA Images. CMES-Computer Modeling in Engineering & Sciences, 125(1), 125–143.
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