Rasterized Steered Mixture of Experts for Efficient 2D Image Regression
This work addresses efficiency limitations in image processing tasks like super-resolution and denoising, offering a practical improvement for researchers and practitioners, though it is incremental as it builds on existing methods.
The paper tackled the high computational cost of the Steered Mixture of Experts framework for 2D image regression by introducing a rasterization-based optimization strategy, achieving significantly faster parameter updates and memory-efficient representations while maintaining reconstruction quality.
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.