基于Voronoi引导的双侧二维高斯点撒法的任意尺度超光谱图像超分辨率
Voronoi-guided Bilateral 2D Gaussian Splatting for Arbitrary-Scale Hyperspectral Image Super-Resolution
AI总结：
本文提出GaussianHSI框架，通过Voronoi引导的双侧二维高斯点撒法实现任意尺度超光谱图像超分辨率，结合谱细节增强模块提升重建效果。
摘要：
现有超光谱图像超分辨率方法需针对不同尺度进行修改，限制了在任意尺度重建中的灵活性。二维高斯点撒提供连续表示，兼容任意尺度超分辨率。现有方法常依赖光栅化策略，可能限制空间建模的灵活性。将其扩展到超光谱图像超分辨率仍具挑战性，因为任务需要自适应空间重建同时保持光谱保真度。本文提出GaussianHSI，一种基于高斯点撒的框架，用于任意尺度超光谱图像超分辨率。我们开发了Voronoi引导的双侧二维高斯点撒法用于空间重建。在预测一组高斯函数表示输入后，通过Voronoi引导选择将每个目标像素与相关高斯函数关联。通过聚合所选高斯函数并利用参考感知的双侧加权重建目标像素，该加权考虑几何相关性和与低分辨率特征的一致性。我们进一步引入了光谱细节增强模块以提升光谱重建。在基准数据集上的广泛实验表明，GaussianHSI在任意尺度超光谱图像超分辨率方面优于现有方法。
英文摘要：
Most existing hyperspectral image super-resolution methods require modifications for different scales, limiting their flexibility in arbitrary-scale reconstruction. 2D Gaussian splatting provides a continuous representation that is compatible with arbitrary-scale super-resolution. Existing methods often rely on rasterization strategies, which may limit flexible spatial modeling. Extending them to hyperspectral image super-resolution remains challenging, as the task requires adaptive spatial reconstruction while preserving spectral fidelity. This paper proposes GaussianHSI, a Gaussian-Splatting-based framework for arbitrary-scale hyperspectral image super-resolution. We develop a Voronoi-Guided Bilateral 2D Gaussian Splatting for spatial reconstruction. After predicting a set of Gaussian functions to represent the input, it associates each target pixel with relevant Gaussian functions through Voronoi-guided selection. The target pixel is then reconstructed by aggregating the selected Gaussian functions with reference-aware bilateral weighting, which considers both geometric relevance and consistency with low-resolution features. We further introduce a Spectral Detail Enhancement module to improve spectral reconstruction. Extensive experiments on benchmark datasets demonstrate the effectiveness of GaussianHSI over state-of-the-art methods for arbitrary-scale hyperspectral image super-resolution.
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