用自适应分段初始化，让图像回归模型少用一半核函数，提速五成。

Kernel image regression methods have shown to provide excellent efficiency in many image processing task, such as image and light-field compression, Gaussian Splatting, denoising and super-resolution. The estimation of parameters for these methods frequently employ gradient descent iterative optimization, which poses significant computational burden for many applications. In this paper, we introduce a novel adaptive segmentation-based initialization method targeted for optimizing Steered-Mixture-of Experts (SMoE) gating networks and Radial-Basis-Function (RBF) networks with steering kernels. The novel initialization method allocates kernels into pre-calculated image segments. The optimal number of kernels, kernel positions, and steering parameters are derived per segment in an iterative optimization and kernel sparsification procedure. The kernel information from "local" segments is then transferred into a "global" initialization, ready for use in iterative optimization of SMoE, RBF, and related kernel image regression methods. Results show that drastic objective and subjective quality improvements are achievable compared to widely used regular grid initialization, "state-of-the-art" K-Means initialization and previously introduced segmentation-based initialization methods, while also drastically improving the sparsity of the regression models. For same quality, the novel initialization results in models with around 50% reduction of kernels. In addition, a significant reduction of convergence time is achieved, with overall run-time savings of up to 50%. The segmentation-based initialization strategy itself admits heavy parallel computation; in theory, it may be divided into as many tasks as there are segments in the images. By accessing only four parallel GPUs, run-time savings of already 50% for initialization are achievable.