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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||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:||https://repository.cihe.edu.hk/jspui/handle/cihe/2305||DOI:||10.1016/j.jvcir.2015.07.006||CIHE Affiliated Publication:||No|
|Appears in Collections:||CIS Publication|
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