Learning-based image interpolation methods have been proved to be effective in imageinterpolation. In this study, the authors propose an accurate imageinterpolation with adaptive k-nearest neighbour searching and no...
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Learning-based image interpolation methods have been proved to be effective in imageinterpolation. In this study, the authors propose an accurate imageinterpolation 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.
V práci je popsán základní princip, vlastnosti a využití interpolace 2D signálu (obrazu). Podrobněji jsou popsány jednotlivé metody interpolace. Teoretické poznatky interp...
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V práci je popsán základní princip, vlastnosti a využití interpolace 2D signálu (obrazu). Podrobněji jsou popsány jednotlivé metody interpolace. Teoretické poznatky interpolace jsou prakticky vyzkoušeny v prostředí Matlab na vybraných metodách interpolace zahrnujících i vlastní implementaci kvadratické a lineární interpolace. Testování je provedeno na různých typech snímků, především pak na biomedicínských. Jednotlivé interpolace jsou také aplikovány na histogram. Zjištěné výsledky jsou navzájem porovnány a přehledně vyneseny do grafických závislostí.
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