Three-dimensional Optical Coherence Tomography Image Denoising via Multi-input Fully-Convolutional Networks
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In recent years, there has been a growing interest in applying convolutional neural networks (CNNs) to low-level vision tasks such as denoising and super-resolution. Optical coherence tomography (OCT) images are inevitably affected by noise, due to the coherent nature of the image formation process. In this paper, we take advantage of the progress in deep learning methods and propose a new method termed multi-input fully-convolutional networks (MIFCN) for denoising of OCT images. Despite recently proposed natural image denoising CNNs, our proposed architecture allows exploiting high degrees of correlation and complementary information among neighboring OCT images through pixel by pixel fusion of multiple FCNs. We also show how the parameters of the proposed architecture can be learned by optimizing a loss function that is specifically designed to take into account consistency between the overall output and the contribution of each input image. We compare the proposed MIFCN method quantitatively and qualitatively with the state-of-the-art denoising methods on OCT images of normal and age-related macular degeneration eyes.
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