In Journal of biophotonics
Optical coherence tomography (OCT) imaging shows a significant potential in clinical routines due to its noninvasive property. However, the quality of OCT images is generally limited by inherent speckle noise of OCT imaging and low sampling rate. To obtain high signal-to-noise ratio (SNR) and high-resolution (HR) OCT images within a short scanning time, we presented a learning-based method to recover high-quality OCT images from noisy and low-resolution OCT images. We proposed a semi-supervised learning approach named N2NSR-OCT, to generate denoised and super-resolved OCT images simultaneously using up- and down-sampling networks (U-Net (Semi) and DBPN (Semi)). Additionally, two different super-resolution and denoising models with different upscale factors (2× and 4×) were trained to recover the high-quality OCT image of the corresponding down-sampling rates. The new semi-supervised learning approach is able to achieve results comparable with those of supervised learning using up- and down-sampling networks, and can produce better performance than other related state-of-the-art methods in the aspects of maintaining subtle fine retinal structures. This article is protected by copyright. All rights reserved.
Qiu Bin, You Yunfei, Huang Zhiyu, Meng Xiangxi, Jiang Zhe, Zhou Chuangqing, Liu Gangjun, Yang Kun, Ren Qiushi, Lu Yanye
denoising, optical coherence tomography, semi-supervised deep learning, super-resolution