Edge-preserving denoising and super-resolution in OCT imagery using deep SMoE gating networks

Aytaç Özkan; Violeta Madjarova; Thomas Sikora; Elena Stoykova

doi:10.1117/12.3017126

20 June 2024 Edge-preserving denoising and super-resolution in OCT imagery using deep SMoE gating networks

Aytaç Özkan, Violeta Madjarova, Thomas Sikora, Elena Stoykova

Proceedings Volume 13006, Biomedical Spectroscopy, Microscopy, and Imaging III; 130061D (2024) https://doi.org/10.1117/12.3017126
Event: SPIE Photonics Europe, 2024, Strasbourg, France

Conference Poster

Abstract

This paper presents an innovative super-resolution (SR) method for Optical Coherence Tomography (OCT), enhancing image resolution and reducing noise without retraining for different scales. Traditional SR techniques, interpolation, reconstruction, and learning-based, are surpassed by our approach, which combines a "shifted steered mixture of experts" with an autoencoder. This method outperforms the latest algorithms in subjective and objective evaluations, including PSNR and perceptual metrics. A distinctive feature is the adjustable sharpness, enabling targeted edge sharpening or defocusing through kernel experts’ bandwidth adjustments. This adaptability negates the need for data-specific retraining, offering a robust solution to improve OCT image quality and medical imaging analysis.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Aytaç Özkan, Violeta Madjarova, Thomas Sikora, and Elena Stoykova "Edge-preserving denoising and super-resolution in OCT imagery using deep SMoE gating networks", Proc. SPIE 13006, Biomedical Spectroscopy, Microscopy, and Imaging III, 130061D (20 June 2024); https://doi.org/10.1117/12.3017126

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