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Optical coherence tomography (OCT) established as an essential part of the diagnosis, monitoring and treatment programs of patients suffering from wet age-related macular degeneration (AMD). To further improve disease progression monitoring and just-in-time therapy, home OCTs such as the innovative self-examination low-cost full-field OCT (SELFF-OCT) are developed, enabling self-examination by patients due to its technical simplicity and cost efficiency, but coming at the cost of reduced image quality indicated by a low signal-to-noise ratio (SNR). Although deep learning denoising methods based on convolutional neural networks (CNN) or generative adversarial networks (GAN) achieve state-of-the-art denoising performance in improving the SNR for better image interpretability, they usually require noise-free images for training, which are not available for OCT imaging or can only be approximated by repeated scanning followed by complex and error-prone registration and multi-frame averaging processes. To circumvent this drawback, the denoising approach proposed in this work is based on utilizing paired SELFF-OCT images acquired from the retina of an artificial eye to train a Noise2Noise (N2N) network by repeatedly mapping one noisy image to another noisy realization of the same image. The performance of the proposed approach is evaluated by denoising unseen SELFF-OCT images from the artificial eye as well as real human eyes, utilizing standard image quality assessment (IQA) metrics as well as non-reference quality metrics. Qualitative and quantitative results of the evaluation verify the effectiveness of the proposed N2N approach by an improved SNR, while important structure information is preserved. Furthermore, the results reveal a superior denoising performance of the proposed approach compared to the application of conventional denoising methods.
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