Seven Ways to Improve Example Based Single Image Super Resolution
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.
2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)