  Optical Coherence Tomography (OCT) image denoising is a fundamental problem
as OCT images suffer from multiplicative speckle noise, resulting in poor
visibility of retinal layers. The traditional denoising methods consider
specific statistical properties of the noise, which are not always known.
Furthermore, recent deep learning-based denoising methods require paired noisy
and clean images, which are often difficult to obtain, especially medical
images. Noise2Noise family architectures are generally proposed to overcome
this issue by learning without noisy-clean image pairs. However, for that,
multiple noisy observations from a single image are typically needed. Also,
sometimes the experiments are demonstrated by simulating noises on clean
synthetic images, which is not a realistic scenario. This work shows how a
single real-world noisy observation of each image can be used to train a
denoising network. Along with a theoretical understanding, our algorithm is
experimentally validated using a publicly available OCT image dataset. Our
approach incorporates Anscombe transform to convert the multiplicative noise
model to additive Gaussian noise to make it suitable for OCT images. The
quantitative results show that this method can outperform several other methods
where a single noisy observation of an image is needed for denoising. The code
and implementation of this paper will be available publicly upon acceptance of
this paper.
