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 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.