Noise as Domain Shift: Denoising Medical Images by Unpaired Image Translation
This addresses denoising for medical imaging, specifically retinal OCT, but is incremental as it adapts an existing method to a new problem.
The paper tackled denoising medical images by framing it as an unpaired domain translation problem between high and low noise domains, using a modified cycleGAN on retinal OCT images, and showed it outperforms other methods in quantitative and qualitative evaluations.
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.