Learning-based image interpolation via robust k-NN searching for coherent AR parameters estimation
Author(s)
Author(s)
Hung, K.-W.
Date Issued
2015
Publisher
Elsevier
Journal
Journal of Visual Communication and Image Representation
Volume
31
Start page
305
End page
311
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.
SFU Affiliated Publication
No
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