mlsanityData to 7 Oct 2026

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

arXiv 2402.12735 ↗First posted 20 Feb 2024eess.IV cs.CVcited by 0

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Abstract (arXiv)

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.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 10 of its 23 references that are in the map. First posted on arXiv 20 Feb 2024.

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References · 10

  1. 2018The Unreasonable Effectiveness of Deep Features as a Perceptual Metriccited by 11,029
  2. 2016Beyond a Gaussian Denoiser: Residual Learning of Deep CNN for Image Denoisingcited by 1,352
  3. 2022Exploring CLIP for Assessing the Look and Feel of Imagescited by 974
  4. 2017FFDNet: Toward a Fast and Flexible Solution for CNN based Image Denoisingcited by 509
  5. 2018Noise2Void - Learning Denoising from Single Noisy Imagescited by 359
  6. 2013Gradient Magnitude Similarity Deviation: A Highly Efficient Perceptual Image Quality Indexcited by 193
  7. 2016Non-Local Color Image Denoising with Convolutional Neural Networkscited by 39
  8. 2022Sparse-based Domain Adaptation Network for OCTA Image Super-Resolution Reconstructioncited by 5
  9. 2023Steered Mixture-of-Experts Autoencoder Design for Real-Time Image Modelling and Denoisingcited by 5
  10. 2023Steered Mixture of Experts Regression for Image Denoising with Multi-Model-Inferencecited by 4

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