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

arXiv: https://arxiv.org/abs/2402.12735 · HTML: https://mlsanity.com/paper/2402.12735/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 10 of its 23 references that are in the map.
- First posted on arXiv 20 Feb 2024.

## Builds on (most cited first, 10 of 10)

- [The Unreasonable Effectiveness of Deep Features as a Perceptual Metric](https://mlsanity.com/paper/1801.03924/) (2018)
- [Beyond a Gaussian Denoiser: Residual Learning of Deep CNN for Image Denoising](https://mlsanity.com/paper/1608.03981/) (2016)
- [Exploring CLIP for Assessing the Look and Feel of Images](https://mlsanity.com/paper/2207.12396/) (2022)
- [FFDNet: Toward a Fast and Flexible Solution for CNN based Image Denoising](https://mlsanity.com/paper/1710.04026/) (2017)
- [Noise2Void - Learning Denoising from Single Noisy Images](https://mlsanity.com/paper/1811.10980/) (2018)
- [Gradient Magnitude Similarity Deviation: A Highly Efficient Perceptual Image Quality Index](https://mlsanity.com/paper/1308.3052/) (2013)
- [Non-Local Color Image Denoising with Convolutional Neural Networks](https://mlsanity.com/paper/1611.06757/) (2016)
- [Sparse-based Domain Adaptation Network for OCTA Image Super-Resolution Reconstruction](https://mlsanity.com/paper/2207.11882/) (2022)
- [Steered Mixture-of-Experts Autoencoder Design for Real-Time Image Modelling and Denoising](https://mlsanity.com/paper/2305.03485/) (2023)
- [Steered Mixture of Experts Regression for Image Denoising with Multi-Model-Inference](https://mlsanity.com/paper/2303.17409/) (2023)

## 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.