Image Super Resolution Using Generative Adversarial Networks and Local Saliency Maps for Retinal Image Analysis View Full Text


Ontology type: schema:Chapter     


Chapter Info

DATE

2017

AUTHORS

Dwarikanath Mahapatra , Behzad Bozorgtabar , Sajini Hewavitharanage , Rahil Garnavi

ABSTRACT

We propose an image super resolution (ISR) method using generative adversarial networks (GANs) that takes a low resolution input fundus image and generates a high resolution super resolved (SR) image upto scaling factor of 16. This facilitates more accurate automated image analysis, especially for small or blurred landmarks and pathologies. Local saliency maps, which define each pixel’s importance, are used to define a novel saliency loss in the GAN cost function. Experimental results show the resulting SR images have perceptual quality very close to the original images and perform better than competing methods that do not weigh pixels according to their importance. When used for retinal vasculature segmentation, our SR images result in accuracy levels close to those obtained when using the original images. More... »

PAGES

382-390

Book

TITLE

Medical Image Computing and Computer-Assisted Intervention − MICCAI 2017

ISBN

978-3-319-66178-0
978-3-319-66179-7

Author Affiliations

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/978-3-319-66179-7_44

DOI

http://dx.doi.org/10.1007/978-3-319-66179-7_44

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1091432173


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