Image interpolation using Shearlet based iterative refinement
Abstract
This paper proposes an image interpolation algorithm exploiting sparse representation for natural images. It involves three main steps: (a) obtaining an initial estimate of the high resolution image using linear methods like FIR filtering, (b) promoting sparsity in a selected dictionary through iterative thresholding, and (c) extracting high frequency information from the approximation to refine the initial estimate. For the sparse modeling, a shearlet dictionary is chosen to yield a multiscale directional representation. The proposed algorithm is compared to several state-of-the-art methods to assess its objective as well as subjective performance. Compared to the cubic spline interpolation method, an average PSNR gain of around 0.8 dB is observed over a dataset of 200 images.
Cite
@article{arxiv.1308.1126,
title = {Image interpolation using Shearlet based iterative refinement},
author = {H. Lakshman and W. -Q Lim and H. Schwarz and D. Marpe and G. Kutyniok and T. Wiegand},
journal= {arXiv preprint arXiv:1308.1126},
year = {2013}
}