Learning-based image interpolation methods have been proved to be effective in image interpolation. In this study, the authors propose an accurate image interpolation with adaptive k -nearest neighbour searching and non-linear regression. The proposed method aims to find k -nearest neighbours of the input image patch and use them to learn the non-linear mapping between low-resolution and high-resolution image patches. To be specific, they first divide the training image patches into many subspaces, then they utilise an adaptive robust and precise k nearest neighbour searching scheme with proposed normalised Gaussian similarity to find the k nearest neighbours in the matched subspace. The selected k image patch pairs are then used to learn the non-linear regression model through an extreme learning machine. Furthermore, the proposed interpolation method is a cascade framework that consists of two stages. Stage 2 takes the results of Stage 1 as input to further improve the performance. Extensive experimental results on commonly used test images and image datasets indicate that their proposed algorithm obtains competitive performance against the state-of-the-art methods both in terms of objective evaluation values and the subjective effect of reconstructed images.

中文翻译：

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自适应图像插值 ķ近邻搜索和随机非线性回归

基于学习的图像插值方法已被证明在图像插值中有效。在这项研究中，作者提出了一种具有自适应的精确图像插值方法ķ 近邻搜索和非线性回归。所提出的方法旨在寻找ķ -将输入图像补丁的最邻近像素相邻，并使用它们来学习低分辨率和高分辨率图像补丁之间的非线性映射。具体来说，他们首先将训练图像块分成许多子空间，然后利用自适应的鲁棒性和精确性ķ 建议的归一化高斯相似度的最近邻搜索方案 ķ匹配子空间中最近的邻居。选定的ķ然后使用图像补丁对通过极限学习机学习非线性回归模型。此外，提出的插值方法是一个由两个阶段组成的级联框架。阶段2将阶段1的结果作为输入，以进一步提高性能。在常用测试图像和图像数据集上的大量实验结果表明，无论是在客观评估值还是在重建图像的主观效果方面，他们提出的算法均相对于最新方法具有竞争优势。