Research map / Video / Video segmentation and tracking
Bringing a Blurry Frame Alive at High Frame-Rate With an Event Camera
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Use EDI to jointly model the blurry frame and event data for deblurring | Single-image deblurring · Synthetic dataset, deblurred images | PSNR(dB): 30.26 -> 29.06 | Hurt |
| Use EDI to jointly model the blurry frame and event data for deblurring | Single-image deblurring · Synthetic dataset, deblurred images | SSIM: 0.9342 -> 0.9430 | Helped |
| Reconstruct video directly with EDI rather than a sequential deblur-and-reconstruction pipeline | High-frame-rate video reconstruction · Synthetic dataset, reconstructed videos | PSNR(dB): 26.34 -> 28.49 | Helped |
| Reconstruct video directly with EDI rather than a sequential deblur-and-reconstruction pipeline | High-frame-rate video reconstruction · Synthetic dataset, reconstructed videos | SSIM: 0.8556 -> 0.9199 | Helped |
Closest papers by meaning
- Latency Correction for Event-guided Deblurring and Frame Interpolation (CVPR 2024 · similarity 0.88)
- EvFocus: Learning to Reconstruct Sharp Images from Out-of-Focus Event Streams (ICML 2025 · similarity 0.87)
- LED: A Large-scale Real-world Paired Dataset for Event Camera Denoising (CVPR 2024 · similarity 0.87)
- Frequency-aware Event-based Video Deblurring for Real-World Motion Blur (CVPR 2024 · similarity 0.87)
- Towards Robust Event-guided Low-Light Image Enhancement: A Large-Scale Real-World Event-Image Dataset and Novel Approach (CVPR 2024 · similarity 0.86)
- E2HQV: High-Quality Video Generation from Event Camera via Theory-Inspired Model-Aided Deep Learning (AAAI 2024 · similarity 0.86)
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