Edge-Aligned Initialization of Kernels for Steered Mixture-of-Experts
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arXiv
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| Hauptverfasser: | , |
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| Format: | Preprint |
| Veröffentlicht: |
2026
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| _version_ | 1866912868391190528 |
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| author | Determann, Martin Fleig, Elvira |
| author_facet | Determann, Martin Fleig, Elvira |
| contents | Steered Mixture-of-Experts (SMoE) has recently emerged as a powerful framework for spatial-domain image modeling, enabling high-fidelity image representation using a remarkably small number of parameters. Its ability to steer kernel-based experts toward structural image features has led to successful applications in image compression, denoising, super-resolution, and light field processing. However, practical adoption is hindered by the reliance on gradient-based optimization to estimate model parameters on a per-image basis - a process that is computationally intensive and difficult to scale. Initialization strategies for SMoE are an essential component that directly affects convergence and reconstruction quality. In this paper, we propose a novel, edge-based initialization scheme that achieves good reconstruction qualities while reducing the need for stochastic optimization significantly. Through a method that leverages Canny edge detection to extract a sparse set of image contours, kernel positions and orientations are deterministically inferred. A separate approach enables the direct estimation of initial expert coefficients. This initialization reduces both memory consumption and computational cost. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2602_02031 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Edge-Aligned Initialization of Kernels for Steered Mixture-of-Experts Determann, Martin Fleig, Elvira Image and Video Processing Steered Mixture-of-Experts (SMoE) has recently emerged as a powerful framework for spatial-domain image modeling, enabling high-fidelity image representation using a remarkably small number of parameters. Its ability to steer kernel-based experts toward structural image features has led to successful applications in image compression, denoising, super-resolution, and light field processing. However, practical adoption is hindered by the reliance on gradient-based optimization to estimate model parameters on a per-image basis - a process that is computationally intensive and difficult to scale. Initialization strategies for SMoE are an essential component that directly affects convergence and reconstruction quality. In this paper, we propose a novel, edge-based initialization scheme that achieves good reconstruction qualities while reducing the need for stochastic optimization significantly. Through a method that leverages Canny edge detection to extract a sparse set of image contours, kernel positions and orientations are deterministically inferred. A separate approach enables the direct estimation of initial expert coefficients. This initialization reduces both memory consumption and computational cost. |
| title | Edge-Aligned Initialization of Kernels for Steered Mixture-of-Experts |
| topic | Image and Video Processing |
| url | https://arxiv.org/abs/2602.02031 |