  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. Due to the coherent nature of the image formation process,
optical coherence tomography (OCT) images are inevitably affected by noise.
This paper proposes a new method named the multi-input fully-convolutional
networks (MIFCN) for denoising of OCT images. In contrast to recently proposed
natural image denoising CNNs, the proposed architecture allows the exploitation
of high degrees of correlation and complementary information among neighboring
OCT images through pixel by pixel fusion of multiple FCNs. The parameters of
the proposed multi-input architecture are learned by considering the
consistency between the overall output and the contribution of each input
image. The proposed MIFCN method is compared with the state-of-the-art
denoising methods adopted on OCT images of normal and age-related macular
degeneration eyes in a quantitative and qualitative manner.
