Learning-based image interpolation via robust k-NN searching for coherent AR parameters estimation

•Learning-based image interpolation using precise and robust k-NN searching for an accurate AR modeling.•Robustness to insufficient k-NN matches and adaptation to relevant k-NN matches during online searching.•Online coherent soft-decision estimation of both local AR parameters and high-resolution p...

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Vydáno v:Journal of visual communication and image representation Ročník 31; s. 305 - 311
Hlavní autoři: Hung, Kwok-Wai, Siu, Wan-Chi
Médium: Journal Article
Jazyk:angličtina
Vydáno: Elsevier Inc 01.08.2015
Témata:
Autoregressive model
Image interpolation
K-means clustering
Learning-based algorithms
Robust estimation
Soft-decision estimation
Super-resolution
Weighted least squares
Soft-decision estimation
Super-resolution
Learning-based algorithms
Autoregressive model
Robust estimation
Weighted least squares
Image interpolation
K-means clustering
ISSN:1047-3203, 1095-9076
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Shrnutí:•Learning-based image interpolation using precise and robust k-NN searching for an accurate AR modeling.•Robustness to insufficient k-NN matches and adaptation to relevant k-NN matches during online searching.•Online coherent soft-decision estimation of both local AR parameters and high-resolution pixels.•Highly competitive performance compared with the state-of-the-art approaches in terms of PSNR and SSIM. Image interpolation is to convert a low-resolution (LR) image into a high-resolution (HR) image through mathematical modeling. An accurate model usually leads to a better reconstruction quality, and the autoregressive (AR) model is a widely adopted model for image interpolation. Although a large amount of works have been done on AR models for image interpolation, there are plenty of rooms for improvements. In this work, we propose a robust and precise k-nearest neighbors (k-NN) searching scheme to form an accurate AR model of the local statistic. We make use of both LR and HR information obtained from a large amount of training data, in order to form a coherent soft-decision estimation of both AR parameters and high-resolution pixels. Experimental results show that the proposed learning-based AR interpolation algorithm has a very competitive performance compared with the state-of-the-art image interpolation algorithms in terms of PSNR and SSIM values.
ISSN:1047-3203
1095-9076
DOI:10.1016/j.jvcir.2015.07.006