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Efficient Non-local Contrastive Attention for Image Super-resolution

AAAI 2022 · Image generation › Super-resolution · approach: Perceptual and real-world SR · 105 citations (OpenAlex)

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Measured effects reported in the paper

What was changedWhereMeasuredEffect
Add ENLCA modules to EDSREDSR · Set5 ×4PSNR: 32.46 vs 32.67Helped
Add ENLA to the baseline (ablation)EDSR baseline · Set5 ×4PSNR: 32.21 -> 32.37Helped
Add amplification factor k to ENLA (ablation)ENLA · Set5 ×4PSNR: 32.37 -> 32.44Helped
Add contrastive learning to ENLA without k (ablation)ENLA · Set5 ×4PSNR: 32.37 -> 32.41Helped
Add both amplification factor k and contrastive learning to ENLA (ablation)ENLA · Set5 ×4PSNR: 32.21 -> 32.48Helped
Increase contrastive positive/negative selection percentage n1 from 1% to 2% at n2=8% (ablation)ENLCA · Set5 ×4PSNR: 32.42 -> 32.48Helped
Increase n1 from 2% to 3% at n2=8% (ablation)ENLCA · Set5 ×4PSNR: 32.48 -> 32.46Hurt
Increase irrelevant-feature start index n2 from 4% to 8% at n1=2% (ablation)ENLCA · Set5 ×4PSNR: 32.45 -> 32.48Helped
Increase n2 from 8% to 20% at n1=2% (ablation)ENLCA · Set5 ×4PSNR: 32.48 -> 32.42Hurt
Increase random feature count from m=2 to m=128 (ablation)ENLCA · Urban100 ×4PSNR: 26.88 -> 27.12Helped
Increase random feature count from m=128 to m=256 (ablation)ENLCA · Urban100 ×4PSNR: 27.12 -> 27.12No clear effect
Use content-relevance sparsity instead of location-fixed criss-cross aggregationnon-local attention · Urban100 ×4PSNR: 26.85 -> 27.12Helped

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