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Tackling the Ill-Posedness of Super-Resolution Through Adaptive Target Generation

CVPR 2021 · Image generation › Super-resolution · approach: Real-world super-resolution · 60 citations (OpenAlex)

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Measured effects reported in the paper

What was changedWhereMeasuredEffect
Train RRDB with adaptive targets instead of only the paired ground-truth targetRRDB blind super-resolution · DIV2KRKLPIPS: 0.3856 -> 0.3394Helped
Train ESRGAN with adaptive targetsESRGAN · Set5, bicubicLPIPS: 0.0736 -> 0.0644Helped
Train ESRGAN with adaptive targetsESRGAN · Set14, bicubicLPIPS: 0.1324 -> 0.1299Helped
Train ESRGAN with adaptive targetsESRGAN · BSDS100, bicubicLPIPS: 0.1643 -> 0.1611Helped
Train ESRGAN with adaptive targetsESRGAN · Urban100, bicubicLPIPS: 0.1236 -> 0.1105Helped
Train ESRGAN with adaptive targetsESRGAN · Manga109, bicubicLPIPS: 0.0645 -> 0.0549Helped
Use translation-only transformations rather than translation plus rotation (ablation)Adaptive target generator · DIV2KRKSSIM: 0.7854 -> 0.7832Hurt
Use integer-step nearest-neighbor search instead of learned sub-pixel affine localization (ablation)Adaptive target generator · DIV2KRKPSNR: 28.42 -> 27.75Hurt
Enlarge the adaptive-target search space to s = 11 (ablation)Adaptive target generator · DIV2KRKPSNR: 28.42 -> 28.27Hurt
Add shear transformations to translation and rotation (ablation)Adaptive target generator · DIV2KRKPSNR: 28.42 -> 28.39Hurt
Add shear and scale transformations to translation and rotation (ablation)Adaptive target generator · DIV2KRKPSNR: 28.42 -> 28.26Hurt
Use adaptive targets without GAN trainingRRDB · Bicubic downsampling, five SR testsetsPSNRMixed

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