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Seven ways to improve example-based single image super resolution

Radu Timofte and Rasmus Rothe and Luc Van Gool
IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR 2016)
US, June 2016

Abstract

In this paper we present seven techniques that everybody should know to improve example-based single image super resolution (SR): 1) augmentation of data, 2) use of large dictionaries with efficient search structures, 3) cascading, 4) image self-similarities, 5) back projection refinement, 6) enhanced prediction by consistency check, and 7) context reasoning. We validate our seven techniques on standard SR benchmarks (i.e. Set5, Set14, B100) and methods (i.e. A+, SRCNN, ANR, Zeyde, Yang) and achieve substantial improvements. The techniques are widely applicable and require no changes or only minor adjustments of the SR methods.Moreover, our Improved A+ (IA) method sets new state-of-the-art results outperforming A+ by up to 0.9dB on average PSNR whilst maintaining a low time complexity.


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@InProceedings{eth_biwi_01294,
  author = {Radu Timofte and Rasmus Rothe and Luc Van Gool},
  title = {Seven ways to improve example-based single image super resolution},
  booktitle = {IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR 2016)},
  year = {2016},
  month = {June},
  keywords = {}
}