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.

本研究引入了一种新型降噪算法，Block-Matching Steered-Mixture of Experts with Multi-Model Inference and Autoencoder (BM-SMoE-AE)，通过将 SMoE 算法的分块匹配实现与增强型自编码器架构相结合，实现了高效的斑点噪声降噪并保留了关键图像细节。与现有的降噪技术相比，BM-SMoE-AE 算法在维持图像完整性和提高医学诊断的 OCT 图像可用性方面表现出卓越的性能。