# Steered Mixture-of-Experts Autoencoder Design for Real-Time Image Modelling and Denoising

arXiv: https://arxiv.org/abs/2305.03485 · HTML: https://mlsanity.com/paper/2305.03485/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Placed in Vision mixture-of-experts, one of 12 directions in Mixture-of-experts.
- Builds on 3 of its 19 references that are in the map.
- First posted on arXiv 5 May 2023.

## Placed in

- Mixture-of-experts / [Vision mixture-of-experts](https://mlsanity.com/moe/vision-mixture-of-experts/)

## Builds on (most cited first, 3 of 3)

- [Deep Convolutional AutoEncoder-based Lossy Image Compression](https://mlsanity.com/paper/1804.09535/) (2018)
- [Edge-Aware Autoencoder Design for Real-Time Mixture-of-Experts Image Compression](https://mlsanity.com/paper/2207.12348/) (2022)
- [Steered Mixture of Experts Regression for Image Denoising with Multi-Model-Inference](https://mlsanity.com/paper/2303.17409/) (2023)

## Built on by (most cited first, 5 of 5)

- [Integration of Mixture of Experts and Multimodal Generative AI in Internet of Vehicles: A Survey](https://mlsanity.com/paper/2404.16356/) (2024)
- [Adaptive Segmentation-Based Initialization for Steered Mixture of Experts Image Regression](https://mlsanity.com/paper/2409.10101/) (2024)
- [Denoising OCT Images Using Steered Mixture of Experts with Multi-Model Inference](https://mlsanity.com/paper/2402.12735/) (2024)
- [Rasterized Steered Mixture of Experts for Efficient 2D Image Regression](https://mlsanity.com/paper/2510.05814/) (2025)
- [Edge-Aligned Initialization of Kernels for Steered Mixture-of-Experts](https://mlsanity.com/paper/2602.02031/) (2026)

## Abstract (arXiv)

Research in the past years introduced Steered Mixture-of-Experts (SMoE) as a framework to form sparse, edge-aware models for 2D- and higher dimensional pixel data, applicable to compression, denoising, and beyond, and capable to compete with state-of-the-art compression methods. To circumvent the computationally demanding, iterative optimization method used in prior works an autoencoder design is introduced that reduces the run-time drastically while simultaneously improving reconstruction quality for block-based SMoE approaches. Coupling a deep encoder network with a shallow, parameter-free SMoE decoder enforces an efficent and explainable latent representation. Our initial work on the autoencoder design presented a simple model, with limited applicability to compression and beyond. In this paper, we build on the foundation of the first autoencoder design and improve the reconstruction quality by expanding it to models of higher complexity and different block sizes. Furthermore, we improve the noise robustness of the autoencoder for SMoE denoising applications. Our results reveal that the newly adapted autoencoders allow ultra-fast estimation of parameters for complex SMoE models with excellent reconstruction quality, both for noise free input and under severe noise. This enables the SMoE image model framework for a wide range of image processing applications, including compression, noise reduction, and super-resolution.