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.

翻译：导向专家混合回归框架在图像重建、压缩、去噪和超分辨率任务中已展现出卓越性能，但其高昂的计算成本限制了实际应用。本研究提出一种基于栅格化的优化策略，将栅格化高斯核渲染的高效性与导向专家混合模型的边缘感知门控机制相结合。所提方法旨在加速二维图像回归过程，同时保持模型固有的稀疏性与重建质量。通过用栅格化公式替代全局迭代优化，该方法实现了显著更快的参数更新和更高内存效率的模型表示。此外，所提框架支持原生超分辨率和图像去噪等应用，这些是标准栅格化高斯核方法无法直接实现的。快速栅格化优化与导向专家混合模型的边缘感知结构相结合，为二维图像处理任务在计算效率与重建保真度之间提供了新的平衡。