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Unsupervised Degradation Representation Learning for Blind Super-Resolution

CVPR 2021 · Image generation › Super-resolution · approach: Blind super-resolution · 418 citations (OpenAlex)

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Measured effects reported in the paper

What was changedWhereMeasuredEffect
Add contrastive degradation representation learning (ablation)DASR · Set14, ×4 SR, isotropic kernel width 3.4PSNR: 26.79 -> 27.58Helped
Use degradation-aware convolutions instead of vanilla convolutions with concatenated degradation features (ablation)DASR · Set14, ×4 SR, isotropic kernel width 3.4PSNR: 26.72 -> 27.58Helped
Add the channel-wise modulation branch to DA convolutions (ablation)DASR · Set14, ×4 SR, isotropic kernel width 3.4PSNR: 27.33 -> 27.58Helped
Provide groundtruth degradation instead of the learned blind representation (ablation)DASR · Set14, ×4 SR, isotropic kernel width 3.4PSNR: 27.58 -> 27.90Helped
Use DASR rather than IKCBlind ×4 super-resolution · Set14, kernel width 3.6PSNR: 26.81 -> 27.45Helped
Use DASR rather than RCANBlind ×4 super-resolution · Set14, kernel width 1.2PSNR: 27.48 -> 28.45Helped
Use DASR rather than RCANBlind ×4 super-resolution · Set14, bicubic degradation (kernel width 0)PSNR: 28.87 -> 28.50Hurt
Use DASR rather than IKCBlind ×4 super-resolution · Set14, kernel width 1.2PSNR: 28.45 -> 28.28Hurt
Use degradation representation learning instead of predictor-based degradation estimationBlind DASR versus SRMDNF + Predictor · Set14, ×4 SR, isotropic kernel width 3.4PSNR: 26.18 -> 27.58Helped

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