Open AccessSimultaneous denoising and super-resolution of optical coherence tomography images based on generative adversarial network
Author(s) -
Yongqiang Huang,
Zexin Lu,
Zhenhua Shao,
Maosong Ran,
Jiliu Zhou,
Leyuan Fang,
Yi Zhang
Publication year - 2019
Publication title -
optics express
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.394
H-Index - 271
ISSN - 1094-4087
DOI - 10.1364/oe.27.012289
Subject(s) - optical coherence tomography , upsampling , speckle noise , computer science , artificial intelligence , speckle pattern , generative adversarial network , image quality , computer vision , noise reduction , coherence (philosophical gambling strategy) , noise (video) , optics , pattern recognition (psychology) , deep learning , image (mathematics) , mathematics , physics , statistics
Optical coherence tomography (OCT) has become a very promising diagnostic method in clinical practice, especially for ophthalmic diseases. However, speckle noise and low sampling rates have intensively reduced the quality of OCT images, which prevents the development of OCT-assisted diagnosis. Therefore, we propose a generative adversarial network-based approach (named SDSR-OCT) to simultaneously denoise and super-resolve OCT images. Moreover, we trained three different super-resolution models with different upscale factors (2× , 4× and 8×) to adapt to the corresponding downsampling rates. We also quantitatively and qualitatively compared our proposed method with some well-known algorithms. The experimental results show that our approach can effectively suppress speckle noise and can super-resolve OCT images at different scales.