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Bringing Alive Blurred Moments

CVPR 2019 · Video › Video segmentation and tracking · approach: Spike and neuromorphic deblurring · 67 citations (OpenAlex)

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Measured effects reported in the paper

What was changedWhereMeasuredEffect
Use the proposed deblurring network instead of the strongest competing methodSingle-image deblurring · GoPro testPSNR: 30.10 -> 30.58Helped
Use the proposed deblurring network instead of the strongest competing methodSingle-image deblurring · GoPro testSSIM: 0.933 -> 0.941Helped
Use the proposed deblurring network instead of the strongest competing methodSingle-image deblurring · GoPro testinference time: 0.4 -> 0.02Helped
Use the proposed deblurring network instead of the strongest competing methodSingle-image deblurring · GoPro testmodel size: 27.5 -> 17.9Helped
Use the proposed video extraction method instead of the competing methodVideo extraction · 7-frame blurambiguity-invariant error: 49.06 -> 44.12Helped
Generate video from a 9-frame blur rather than a 7-frame blurOurs · Video extractionambiguity-invariant error: 44.12 -> 48.24Mixed
Estimate optical flow rather than directly estimate frame intensities (ablation)Video autoencoder · Video reconstructionvideo generation qualityHelped
Use a recurrent decoder to generate the sequence (ablation)Video extraction · Video generationtemporal consistencyHelped
Train BIE with a pretrained RVD in the two-stage strategy rather than train BIE and RVD directly from scratch (ablation)BIE-RVD · Video extractionvideo generation qualityHelped
Provide the central sharp frame as an additional BIE input (ablation)BIE · Video extractionvideo generation qualityHelped

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