CycleGAN-based deep learning technique for artifact reduction in fundus photography
Author:
Publisher
Springer Science and Business Media LLC
Subject
Cellular and Molecular Neuroscience,Sensory Systems,Ophthalmology
Link
https://link.springer.com/content/pdf/10.1007/s00417-020-04709-5.pdf
Reference29 articles.
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2. Bartling H, Wanger P, Martin L (2009) Automated quality evaluation of digital fundus photographs. Acta Ophthalmol 87:643–647. https://doi.org/10.1111/j.1755-3768.2008.01321.x
3. Marrugo AG, Sorel M, Sroubek F, Millán MS (2011) Retinal image restoration by means of blind deconvolution. J Biomed Opt 16:116016. https://doi.org/10.1117/1.3652709
4. Mora AD, Soares J, Fonseca JM (2013) A template matching technique for artifacts detection in retinal images. In: 2013 8th international symposium on image and signal processing and analysis (ISPA). pp 717–722
5. Gondara L (2016) Medical image denoising using convolutional denoising autoencoders. In: 2016 IEEE 16th international conference on data mining workshops (ICDMW). pp 241–246
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