用栅格化优化加速图像回归，兼顾速度与重建质量。
Rasterized Steered Mixture of Experts for Efficient 2D Image Regression
- 将全局迭代优化改为栅格化计算，提升更新速度。
- 在保持稀疏性的同时，实现高效图像超分辨率与去噪。
- 适合需要快速高质量图像处理的实时应用。
Steered Mixture of Experts 回归框架在图像重建、压缩、去噪和超分辨率任务中表现优异，但计算开销大限制了实际应用。本文提出一种基于栅格化的优化策略，融合栅格化高斯核渲染的效率与 Steered Mixture of Experts 的边缘感知门控机制。通过将全局迭代优化替换为栅格化形式，方法实现了更快的参数更新和更紧凑的内存表示。同时，该框架支持原生超分辨率与图像去噪，是标准栅格化高斯核方法无法直接实现的功能。结合快速栅格化优化与边缘感知结构，为二维图像处理任务提供了计算效率与重建保真度的新平衡。
原文摘要 · Abstract (English)
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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