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+, SR-CNN, 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.
REF exploits seven ways to improve the performance of general example-based ISR methods.
Seven Ways to Improve Example-Based Single Image Super Resolution
2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
Computer Science