English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

Optical Coherence Tomography (OCT) image denoising is a fundamental problemas OCT images suffer from multiplicative speckle noise, resulting in poorvisibility of retinal layers. The traditional denoising methods considerspecific statistical properties of the noise, which are not always known.Furthermore, recent deep learning-based denoising methods require paired noisyand clean images, which are often difficult to obtain, especially medicalimages. Noise2Noise family architectures are generally proposed to overcomethis 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 cleansynthetic images, which is not a realistic scenario. This work shows how asingle real-world noisy observation of each image can be used to train adenoising network. Along with a theoretical understanding, our algorithm isexperimentally validated using a publicly available OCT image dataset. Ourapproach incorporates Anscombe transform to convert the multiplicative noisemodel to additive Gaussian noise to make it suitable for OCT images. Thequantitative results show that this method can outperform several other methodswhere a single noisy observation of an image is needed for denoising. The codeand implementation of this paper will be available publicly upon acceptance ofthis paper.

De-speckling of Optical Coherence Tomography Images Using Anscombe Transform and a Noisier2noise Model