Denoising OCT Images Using Steered Mixture of Experts with Multi-Model Inference

arXiv:2402.12735v1 Announce Type: cross
Abstract: 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. …

abstract accuracy algorithm arxiv block challenges cs.cv current deep learning deep learning techniques denoising diagnostic eess.iv experts filtering image images inference limitations mixture of experts noise novel optical preservation quality study type