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Learning Omni-Frequency Region-adaptive Representations for Real Image Super-Resolution

AAAI 2021 · Image generation › Super-resolution · approach: Real-world super-resolution · 40 citations (OpenAlex)

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Measured effects reported in the paper

What was changedWhereMeasuredEffect
Add OR-Net's frequency decomposition and region-adaptive aggregationRealSR models · DRealSR ×2PSNR: 34.45 -> 34.56Helped
Add OR-Net's frequency decomposition and region-adaptive aggregationRealSR models · DRealSR ×3PSNR: 33.06 -> 33.28Helped
Add OR-Net's frequency decomposition and region-adaptive aggregationRealSR models · DRealSR ×4PSNR: 32.42 -> 32.59Helped
Add OR-Net's frequency decomposition and region-adaptive aggregationRealSR models · RealSR ×2PSNR: 33.96 -> 34.08Helped
Add RFA (ablation)OR-Net with FEU · DRealSR ×4PSNR: 32.34 -> 32.59Helped
Add FEU (ablation)OR-Net with RFA · DRealSR ×4PSNR: 32.23 -> 32.59Helped
Add RFA and FEU (ablation)OR-Net without both modules · DRealSR ×4PSNR: 32.11 -> 32.59Helped
Replace spatial attention with dynamic-convolution RFA (ablation)OR-Net frequency aggregation · DRealSR ×4PSNR: 32.46 -> 32.59Helped
Increase frequency branches from 1 to 2 (ablation)OR-Net FD · DRealSR ×4PSNR: 32.25 -> 32.40Helped
Increase frequency branches from 2 to 3 (ablation)OR-Net FD · DRealSR ×4PSNR: 32.40 -> 32.59Helped
Increase frequency branches from 3 to 4 (ablation)OR-Net FD · DRealSR ×4PSNR: 32.59 -> 32.52Hurt
Use three frequency branches rather than one (ablation)OR-Net FD · DRealSR ×4PSNR: 32.25 -> 32.59Helped

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