N2NSR‐OCT: Simultaneous denoising and super‐resolution in optical coherence tomography images using semisupervised deep learning,Journal of Biophotonics

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N2NSR‐OCT: Simultaneous denoising and super‐resolution in optical coherence tomography images using semisupervised deep learning
Journal of Biophotonics ( IF 2.0 ) Pub Date : 2020-10-07 , DOI: 10.1002/jbio.202000282
Bin Qiu _{1,

2,

3} , Yunfei You _{1,

2,

3} , Zhiyu Huang _{1,

2,

3} , Xiangxi Meng _{1,

4} , Zhe Jiang _{1,

2,

3} , Chuanqing Zhou ₃ , Gangjun Liu _{2,

3} , Kun Yang ₅ , Qiushi Ren _{1,

2,

3} , Yanye Lu _{1,

2,

3,

6}

Affiliation

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 semisupervised 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 semisupervised 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.

中文翻译：

N2NSR-OCT：使用半监督深度学习在光学相干断层扫描图像中同时进行去噪和超分辨率

由于其无创性，光学相干断层扫描（OCT）成像在临床常规检查中显示出巨大潜力。但是，OCT图像的质量通常受到OCT成像的固有斑点噪声和低采样率的限制。为了在短时间内获得高信噪比（SNR）和高分辨率（HR）OCT图像，我们提出了一种基于学习的方法，可从嘈杂的低分辨率OCT图像中恢复高质量的OCT图像。我们提出了一种名为N2NSR-OCT的半监督学习方法，以使用上采样和下采样网络（U-Net（Semi）和DBPN（Semi））同时生成去噪和超分辨的OCT图像。此外，具有不同放大系数（2 ×和4 ×的两个不同的超分辨率和降噪模型））经过了培训，可以恢复相应下采样率的高质量OCT图像。这种新的半监督学习方法能够获得与使用上下采样网络进行的监督学习相当的结果，并且在保持微妙的视网膜结构方面，可以比其他相关的最新技术产生更好的性能。。

更新日期：2020-10-07

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