Seven Ways to Improve Example-Based Single Image Super Resolution

Radu Timofte, Rasmus Rothe, Luc Van Gool; The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016, pp. 1865-1873

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.

Related Material


[pdf]
[bibtex]
@InProceedings{Timofte_2016_CVPR,
author = {Timofte, Radu and Rothe, Rasmus and Van Gool, Luc},
title = {Seven Ways to Improve Example-Based Single Image Super Resolution},
booktitle = {The IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
year = {2016}
}