  We cast the problem of image denoising as a domain translation problem
between high and low noise domains. By modifying the cycleGAN model, we are
able to learn a mapping between these domains on unpaired retinal optical
coherence tomography images. In quantitative measurements and a qualitative
evaluation by ophthalmologists, we show how this approach outperforms other
established methods. The results indicate that the network differentiates
subtle changes in the level of noise in the image. Further investigation of the
model's feature maps reveals that it has learned to distinguish retinal layers
and other distinct regions of the images.
