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A super-resolution method of retinal image based on laser scanning ophthalmoscope
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Paper Abstract

The Scanning Laser Ophthalmoscope (SLO) is an essential medical tool for diagnosis of retinal disease. It uses a small amount of laser to scan the retinal at high speed and transmits the fundus images to the video monitor for medical auxiliary diagnosis. However, like all optical imaging technologies, due to the interference of hardware equipment and external conditions, it is often not ideal imaging. In most clinical cases of laser ophthalmoscope, only low-resolution retinal images can be used to assist medical diagnosis. For this reason, we propose a new depth super-resolution method of retinal image based on laser scanning ophthalmoscope. The retinal image enhanced by local Laplacian operator is introduced into an efficient full convolution neural network. The convolution network uses Adam algorithm to replace the traditional SGD(Stochastic gradient descent) method, which runs faster and faster, and the reconstructed image effect is better. In this work, we subjectively evaluate our algorithm, apply it to real retinal images and compare it with several traditional super-resolution reconstruction methods. The experimental results show that this method has achieved good results in improving the overall quality of laser scanning ophthalmoscope image.

Paper Details

Date Published: 18 December 2019
PDF: 8 pages
Proc. SPIE 11342, AOPC 2019: AI in Optics and Photonics, 1134206 (18 December 2019); doi: 10.1117/12.2542197
Show Author Affiliations
Zelin Chen, Shenzhen Univ. (China)
Xingzheng Wang, Shenzhen Univ. (China)
Yuanlong Deng, Shenzhen Univ. (China)


Published in SPIE Proceedings Vol. 11342:
AOPC 2019: AI in Optics and Photonics
John Greivenkamp; Jun Tanida; Yadong Jiang; HaiMei Gong; Jin Lu; Dong Liu, Editor(s)

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