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arXiv · 2510.05814

Rasterized Steered Mixture of Experts for Efficient 2D Image Regression

Yi-Hsin Li·Mårten Sjöström·Sebastian Knorr·Thomas Sikora
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Abstract

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.

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Yi-Hsin Li, Mårten Sjöström, Sebastian Knorr, Thomas Sikora. 2025-11-18. Rasterized Steered Mixture of Experts for Efficient 2D Image Regression. https://arxiv.org/abs/2510.05814

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