# Rasterized Steered Mixture of Experts for Efficient 2D Image Regression

arXiv: https://arxiv.org/abs/2510.05814 · HTML: https://mlsanity.com/paper/2510.05814/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Placed in Vision mixture-of-experts, one of 12 directions in Mixture-of-experts.
- Builds on 16 of its 48 references that are in the map.
- First posted on arXiv 7 Oct 2025.

## Placed in

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

## Builds on (most cited first, 16 of 16)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [3D Gaussian Splatting for Real-Time Radiance Field Rendering](https://mlsanity.com/paper/2308.04079/) (2023)
- [Diffusion Models in Vision: A Survey](https://mlsanity.com/paper/2209.04747/) (2022)
- [Plug-and-Play Image Restoration with Deep Denoiser Prior](https://mlsanity.com/paper/2008.13751/) (2020)
- [DynIBaR: Neural Dynamic Image-Based Rendering](https://mlsanity.com/paper/2211.11082/) (2022)
- [GaussianImage: 1000 FPS Image Representation and Compression by 2D Gaussian Splatting](https://mlsanity.com/paper/2403.08551/) (2024)
- [Solving Inverse Problems With Deep Neural Networks -- Robustness Included?](https://mlsanity.com/paper/2011.04268/) (2020)
- [Learning Regularization Parameter-Maps for Variational Image Reconstruction using Deep Neural Networks and Algorithm Unrolling](https://mlsanity.com/paper/2301.05888/) (2023)
- [Software Rasterization of 2 Billion Points in Real Time](https://mlsanity.com/paper/2204.01287/) (2022)
- [Edge-Aware Autoencoder Design for Real-Time Mixture-of-Experts Image Compression](https://mlsanity.com/paper/2207.12348/) (2022)
- [Overfitting Behaviour of Gaussian Kernel Ridgeless Regression: Varying Bandwidth or Dimensionality](https://mlsanity.com/paper/2409.03891/) (2024)
- [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)
- [Adaptive Segmentation-Based Initialization for Steered Mixture of Experts Image Regression](https://mlsanity.com/paper/2409.10101/) (2024)
- [4-D Epanechnikov Mixture Regression in Light Field Image Compression](https://mlsanity.com/paper/2108.06464/) (2021)
- [Sparse Video Representation Using Steered Mixture-of-Experts With Global Motion Compensation](https://mlsanity.com/paper/2209.05993/) (2022)

## Abstract (arXiv)

The Steered Mixture of Experts regression framework has demonstrated strong performance in image reconstruction, compression, denoising, and super-resolution. However, its high computational cost limits practical applications. This work introduces a rasterization-based optimization strategy that combines the efficiency of rasterized Gaussian kernel rendering with the edge-aware gating mechanism of the Steered Mixture of Experts. The proposed method is designed to accelerate two-dimensional image regression while maintaining the model's inherent sparsity and reconstruction quality. By replacing global iterative optimization with a rasterized formulation, the method achieves significantly faster parameter updates and more memory-efficient model representations. In addition, the proposed framework supports applications such as native super-resolution and image denoising, which are not directly achievable with standard rasterized Gaussian kernel approaches. The combination of fast rasterized optimization with the edge-aware structure of the Steered Mixture of Experts provides a new balance between computational efficiency and reconstruction fidelity for two-dimensional image processing tasks.