  Optical coherence tomography (OCT) is a prevalent non-invasive imaging method
which provides high resolution volumetric visualization of retina. However, its
inherent defect, the speckle noise, can seriously deteriorate the tissue
visibility in OCT. Deep learning based approaches have been widely used for
image restoration, but most of these require a noise-free reference image for
supervision. In this study, we present a diffusion probabilistic model that is
fully unsupervised to learn from noise instead of signal. A diffusion process
is defined by adding a sequence of Gaussian noise to self-fused OCT b-scans.
Then the reverse process of diffusion, modeled by a Markov chain, provides an
adjustable level of denoising. Our experiment results demonstrate that our
method can significantly improve the image quality with a simple working
pipeline and a small amount of training data.
