1. Unsupervised Denoising of Optical Coherence Tomography Images with Nonlocal-Generative Adversarial Network

    Unsupervised Denoising of Optical Coherence Tomography Images with Nonlocal-Generative Adversarial Network

    Deep learning for image denoising has recently attracted considerable attentions due to its excellent performance. Since most of current deep learning based denoising models require a large number of clean images for training, it is difficult to extend them to the denoising problems when the reference clean images are hard to acquire (e.g., optical coherence tomography (OCT) images). In this paper, we propose a novel unsupervised deep learning model called as nonlocal-generative adversarial network (Nonlocal-GAN) for OCT image denoising, where the deep model can be trained without reference clean images. Specifically, considering that the background areas of OCT images ...

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