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Learning Event-Based Motion Deblurring

CVPR 2020 · Video › Video segmentation and tracking · approach: Event-based video reconstruction · 198 citations (OpenAlex)

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Measured effects reported in the paper

What was changedWhereMeasuredEffect
Use the recurrent event-assisted deblurring method rather than the strongest listed baselineGoPro single-image deblurring · GoProPSNR: 31.50 -> 31.79Helped
Use the recurrent event-assisted deblurring method rather than the strongest listed baselineGoPro video reconstruction · GoProPSNR: 28.49 -> 29.67Helped
Use the recurrent event-assisted deblurring method rather than the strongest listed baselineBlur-DVS single-image deblurring · Blur-DVSPSNR: 26.08 -> 26.48Helped
Use the recurrent event-assisted deblurring method rather than the strongest listed baselineBlur-DVS video reconstruction · Blur-DVSPSNR: 24.10 -> 25.33Helped
Add event input to blurred-image appearance (ablation)Blur-DVS deblurring · Appearance-only modelPSNR: 16.50 -> 23.39Helped
Add blurred-image appearance to events and motion compensation (ablation)Blur-DVS deblurring · Events + MC modelPSNR: 16.38 -> 24.71Helped
Add motion compensation (ablation)Blur-DVS deblurring · Appearance + eventsPSNR: 23.39 -> 24.71Helped
Add directional event filtering boundary guidance (ablation)Blur-DVS deblurring · Appearance + events + MCPSNR: 24.71 -> 25.33Helped
Add directional event filtering boundary guidance (ablation)Blur-DVS deblurring · Full method versus without guidanceSSIM: 0.786 -> 0.827Helped
Use boundary guidance alone, without event features in each sequential step (ablation)Blur-DVS deblurring · Guidance-only variantSSIM: 0.786 -> 0.816Helped
Replace DEF with additional convolution layers in the process encoder (ablation)Blur-DVS deblurring · Extra-parameter variantSSIM: 0.786 -> 0.788No clear effect

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