Learning-based denoising and fringe removal in time-domain full-field OCT in vivo images

Ahmed Ben Aissa; Kate Grieve; Claude Boccara; Viacheslav Mazlin

doi:10.1117/12.2650667

17 March 2023 Learning-based denoising and fringe removal in time-domain full-field OCT in vivo images

Ahmed Ben Aissa, Kate Grieve, Claude Boccara, Viacheslav Mazlin

Author Affiliations +

Proceedings Volume PC12360, Ophthalmic Technologies XXXIII; PC1236007 (2023) https://doi.org/10.1117/12.2650667
Event: SPIE BiOS, 2023, San Francisco, California, United States

Abstract

We propose a robust deep learning algorithm for denoising TD FF-OCT in vivo images. This algorithm does not require any clean images in its training. It specifically detects and removes residual fringes as well as other types of noise present in in vivo eye images. It can also be trained using ex vivo images as well as simulated patterns for fringe removal. Testing is performed on in vivo corneal images, but can be expanded to any TD FF-OCT images. The obtained outputs are thus easier to interpret and exploit in clinical practice as well as other image processing tasks.

Conference Presentation

Citation Download Citation

Ahmed Ben Aissa, Kate Grieve, Claude Boccara, and Viacheslav Mazlin "Learning-based denoising and fringe removal in time-domain full-field OCT in vivo images", Proc. SPIE PC12360, Ophthalmic Technologies XXXIII, PC1236007 (17 March 2023); https://doi.org/10.1117/12.2650667

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