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- Title
Image interpolation with adaptive k‐nearest neighbours search and random non‐linear regression.
- Authors
Zheng, Jieying; Song, Wanru; Wu, Yahong; Liu, Feng
- Abstract
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.
- Publication
IET Image Processing (Wiley-Blackwell), 2020, Vol 14, Issue 8, p1539
- ISSN
1751-9659
- Publication type
Article
- DOI
10.1049/iet-ipr.2019.1591