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Title: Learning-based image interpolation via robust k-NN searching for coherent AR parameters estimation
Author(s): Siu, Wan Chi 
Author(s): Hung, K.-W.
Issue Date: 2015
Publisher: Elsevier
Journal: Journal of Visual Communication and Image Representation 
Volume: 31
Start page: 305
End page: 311
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.
DOI: 10.1016/j.jvcir.2015.07.006
CIHE Affiliated Publication: No
Appears in Collections:CIS Publication

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