Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/11844
Title: Learning-based image interpolation via robust k-NN searching for coherent AR parameters estimation
Authors: Hung, KW
Siu, WC 
Keywords: Autoregressive model
Image interpolation
K-means clustering
Learning-based algorithms
Robust estimation
Soft-decision estimation
Super-resolution
Weighted least squares
Issue Date: 2015
Publisher: Academic Press
Source: Journal of visual communication and image representation, 2015, v. 31, p. 305-311 How to cite?
Journal: Journal of visual communication and image representation 
Abstract: 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.
URI: http://hdl.handle.net/10397/11844
ISSN: 1047-3203
DOI: 10.1016/j.jvcir.2015.07.006
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