Steered Mixture of Expert (SMoE) represents a pioneering approach in image processing, with the central objective of preserving edge details and textures while ensuring a visually smooth transition between image regions. Initialization, a fundamental step in SMoE, is pivotal in influencing the algorithm's success. The choice of how SMoE starts its optimization process can greatly influence its convergence, efficiency, and the quality of results. Recognizing the significance of initialization, this paper introduces a groundbreaking enhancement to SMoE through segment-wise initialization. Motivated by the intricate nature of real-world images, characterized by diverse content and high-frequency details, this approach tailors initialization parameters for each segment while dynamically adapting the number of kernels. One pivotal advantage of segment-wise initialization is its specialized efficacy in preserving high-frequency information within images. By customizing initialization parameters to specifically address high-frequency details, this approach excels in capturing intricate textures, edges, and other fine details crucial for the fidelity of reconstructed images. Beyond its high-frequency preservation capabilities, segment-wise initialization offers fine-grained control over parameter customization, ensuring each image segment is treated with precision according to its unique content characteristics. Furthermore, segment-wise initialization emphasizes the accelerated optimization process resulting from well-initialized parameters. In a comprehensive statistical analysis, the benefits of segment-wise initialization become evident. When compared to a state-of-the-art method under a constant number of kernels, our approach achieves a noteworthy 1dB gain in Peak Signal-to-Noise Ratio (PSNR), demonstrating a significant improvement in image quality. Additionally, the efficiency gains are striking, with segment-wise initialization resulting in a remarkable 40% reduction in optimization time compared to the existing state-of-the-art method. This research recognizes that the benefits of segment-wise initialization extend beyond enhanced image quality, and the adaptability of this strategy proves crucial in scenarios where images exhibit heterogeneity, responding adeptly to the diverse characteristics of different regions within a single image. This not only contributes to computational efficiency but also positions segment-wise initialization as a key enabler for SMoE's broader applicability and performance across diverse image processing tasks.