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Jiawei Ma

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Proceedings Article | 19 December 2022 Poster

Unsupervised speckle reduction of retinal optical coherence tomography images via edge-enhanced generative adversarial network

Zongqing Ma, Jiawei Ma, Xudong Chen, Xiaochen Meng, Jiang Zhu

Proceedings Volume 12320, 123201J (2022) https://doi.org/10.1117/12.2642418

KEYWORDS: Optical coherence tomography, Speckle, Performance modeling, Eye models, Data modeling, Image quality, Image enhancement, Image denoising, Denoising

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Speckle noise in optical coherence tomography (OCT) images seriously degrades the image quality and impairs the subsequent diagnosis of various ocular diseases. Most of the existing deep learning-based denoising models pay little attention to edge preservation, and rely on the large number of reference clean images which are hard to acquire in clinical OCT practice. In this work, an unsupervised retinal OCT image denoising model, named as edge-enhanced generative adversarial network (EEGAN), is proposed to free the dependence on reference clean images and enhance the edge information. Specifically, considering the noisy OCT image can be roughly divided into noisy retinal foreground and noise-only background regions, the generator of EEGAN is designed to denoise the noisy foreground samples based on the residual dense blocks, while the discriminator of EEGAN is employed to distinguish the real background noise samples from the fake noise samples, i.e., the difference images between the noisy foreground samples and its generated counterparts. As retinal edge details are the most vital information for disease diagnosis, an edge enhancement layer based on Sobel operators is integrated into the generator of EEGAN to strengthen the edge preservation ability of the model. Experimental results on clinical retinal OCT datasets show that our model has a better performance than the compared models in suppressing noise and preserving details, demonstrating the effectiveness of the proposed EEGAN.

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