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ARVo: Learning All-Range Volumetric Correspondence for Video Deblurring

CVPR 2021 · Video › Video segmentation and tracking · approach: Learned optical flow estimation · 76 citations (OpenAlex)

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Measured effects reported in the paper

What was changedWhereMeasuredEffect
Use ARVo instead of the strongest listed baselineVideo deblurring · DVD testPSNR: 32.13 -> 32.80Helped
Use ARVo instead of the strongest listed baselineVideo deblurring · HFR-DVD testPSNR: 29.71 -> 31.15Helped
Add correlation-volume features rather than using none (ablation)ARVo · DVD testPSNR: 31.85 -> 32.45Helped
Increase correlation-pyramid depth from 1 to 3 layers (ablation)ARVo · DVD testPSNR: 32.45 -> 32.80Helped
Increase correlation-pyramid depth from 3 to 4 layers (ablation)ARVo · DVD testPSNR: 32.80 -> 32.81No clear effect
Add optical-flow alignment to correlation-volume alignment (ablation)ARVo · DVD testPSNR: 32.01 -> 32.80Helped
Add correlation-volume alignment to optical-flow alignment (ablation)ARVo · DVD testPSNR: 31.85 -> 32.80Helped
Train progressively with 2 stages instead of 1 (ablation)ARVo · DVD testPSNR: 32.28 -> 32.80Helped
Train progressively with 3 stages instead of 2 (ablation)ARVo · DVD testSSIM: 0.9352 -> 0.9349Hurt
Add adversarial training (ablation)ARVo · DVD testPSNR: 32.80 -> 32.79 (-0.01)No clear effect
Add adversarial training (ablation)ARVo · DVD testSSIM: 0.9352 -> 0.9354 (+0.02)No clear effect

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