Denoising OCT Images Using Steered Mixture of Experts with Multi-Model Inference
This addresses noise reduction for medical diagnostics in OCT imaging, but it appears incremental as it builds on prior techniques like SMoE and autoencoders.
The study tackled speckle noise in Optical Coherence Tomography (OCT) images, which impairs diagnostic accuracy, by introducing the BM-SMoE-AE algorithm, resulting in improved edge definition and reduced processing time compared to existing methods.
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.