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... (read more)

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Datasets


TASK DATASET MODEL METRIC NAME METRIC VALUE GLOBAL RANK RESULT BENCHMARK
Image Super-Resolution BSD100 - 4x upscaling IA PSNR 27.16 # 29
Image Super-Resolution Set14 - 4x upscaling IA PSNR 27.88 # 34
Image Super-Resolution Set5 - 4x upscaling IA PSNR 31.10 # 31

Methods used in the Paper


METHOD TYPE
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