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.

翻译：在光学相干断层扫描（OCT）中，散斑噪声严重削弱图像质量，影响诊断准确性。当前方法（包括传统滤波和深度学习技术）在噪声抑制与细节保留方面存在局限性。针对这些挑战，本研究提出一种新型去噪算法——基于多模型推理与自编码器的块匹配导向混合专家模型（BM-SMoE-AE）。该方法将SMoE算法的块匹配实现与增强型自编码器架构相结合，在保留关键图像细节的同时实现高效散斑噪声抑制。本方法的突出优势在于增强边缘清晰度并缩短处理时间。与现有去噪技术的对比分析表明，BM-SMoE-AE在维持图像完整性及提升OCT图像在医学诊断中的可用性方面表现优异。