Single-image super-resolution (SR) imaging is a fundamental problem in image processing; it is important in entertainment, video surveillance, remote sensing, medicine, and other fields. Gaussian process regression (GPR) is a kernel method whereby nonlinear mapping relationships in data can be learned. However, the traditional Gaussian kernel function used in GPR is isotropic and fails to capture complex image structures. Accordingly, the structure information of image patches, termed steering kernel coefficients (SKCs), is extracted by a steering kernel function. After patches with similar structure are clustered according to their SKCs, an anisotropic automatic-relevance-determination (ARD) kernel function is used to learn the model for each cluster. Aiming at learning a structure-sensitive GPR model, we integrate the SKCs and ARD to achieve improved performance for GPR-based SR. Experiments demonstrate that the proposed method can effectively capture the structural relevance of image patches and yield promising results.

中文翻译：

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基于转向核和高斯过程回归的单图像超分辨率

单幅图像超分辨率（SR）成像是图像处理中的一个基本问题；在娱乐、视频监控、遥感、医学等领域具有重要意义。高斯过程回归（GPR）是一种核方法，可以学习数据中的非线性映射关系。然而，GPR 中使用的传统高斯核函数是各向同性的，无法捕捉复杂的图像结构。因此，图像块的结构信息，称为转向核系数（SKC），由转向核函数提取。在根据其 SKC 对具有相似结构的补丁进行聚类后，使用各向异性自动相关性确定 (ARD) 核函数来学习每个聚类的模型。旨在学习结构敏感的 GPR 模型，我们集成了 SKC 和 ARD，以提高基于 GPR 的 SR 的性能。实验表明，所提出的方法可以有效地捕捉图像块的结构相关性并产生有希望的结果。