In Optical Coherence Tomography (OCT), speckle noise significantly hampers image quality, affecting diagnostic accuracy. Current methods, including traditional filtering and deep learning techniques, have limitations in noise reduction and detail preservation. Addressing these challenges, this study introduces a novel denoising algorithm, Block-Matching Steered-Mixture of Experts with Multi-Model Inference and Autoencoder (BM-SMoE-AE). This method combines block-matched implementation of the SMoE algorithm with an enhanced autoencoder architecture, offering efficient speckle noise reduction while retaining critical image details. Our method stands out by providing improved edge definition and reduced processing time. Comparative analysis with existing denoising techniques demonstrates the superior performance of BM-SMoE-AE in maintaining image integrity and enhancing OCT image usability for medical diagnostics.
@article{arxiv.2402.12735,
title = {Denoising OCT Images Using Steered Mixture of Experts with Multi-Model Inference},
author = {Aytaç Özkan and Elena Stoykova and Thomas Sikora and Violeta Madjarova},
journal= {arXiv preprint arXiv:2402.12735},
year = {2024}
}
Comments
This submission contains 10 pages and 4 figures. It was presented at the 2024 SPIE Photonics West, held in San Francisco. The paper details advancements in photonics applications related to healthcare and includes supplementary material with additional datasets for review