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Research map / Graph learning / Knowledge and temporal graphs

Rain Streak Removal via Dual Graph Convolutional Network

AAAI 2021 · Graph learning › Knowledge and temporal graphs · approach: Graph reasoning for visual recognition · 153 citations (OpenAlex)

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Measured effects reported in the paper

What was changedWhereMeasuredEffect
Use dual spatial and channel GCNs instead of the baseline without GCNs (ablation)Dual GCN · Rain200HPSNR: 29.13 -> 31.15Helped
Add channel GCN to the baseline (ablation)Rain-removal network · Rain200HPSNR: 29.13 -> 30.53Helped
Add spatial GCN to the baseline (ablation)Rain-removal network · Rain200HPSNR: 29.13 -> 30.61Helped
Increase kernel count and unit count (ablation)Dual GCN · Rain200HPSNR: 29.17 -> 31.37Helped
Use the full Dual GCN method rather than the strongest listed baselineRain-removal methods · Rain12PSNR: 37.86 -> 38.99Helped
Use the full Dual GCN method rather than the strongest listed baselineRain-removal methods · Rain200LPSNR: 39.17 -> 40.73Helped
Use the full Dual GCN method rather than the strongest listed baselineRain-removal methods · Rain200HPSNR: 30.24 -> 31.15Helped
Use the full Dual GCN method rather than the strongest listed baselineRain-removal methods · DID-DataPSNR: 34.68 -> 34.37Hurt
Use the full Dual GCN method rather than the strongest listed baselineRain-removal methods · DDN-DataPSNR: 33.05 -> 33.01Hurt
Use the full Dual GCN method rather than the strongest listed baselineRain-removal methods · SPA-DataPSNR: 43.36 -> 44.18Helped

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