Seven Ways to Improve Example-Based Single Image Super Resolution

• Published in: 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) pp. 1865 - 1873
• Main Authors: Timofte, Radu, Rothe, Rasmus, Van Gool, Luc
• Format: Conference Proceeding
• Language: English
• Published: IEEE 01.06.2016
• Subjects: Dictionaries; Encoding; Image reconstruction; Image resolution; Testing; Training
• DOI: 10.1109/CVPR.2016.206

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 stateof-the-art results outperforming A+ by up to 0.9dB on average PSNR whilst maintaining a low time complexity.