Papers
Topics
Authors
Recent
Gemini 2.5 Flash
Gemini 2.5 Flash
167 tokens/sec
GPT-4o
7 tokens/sec
Gemini 2.5 Pro Pro
42 tokens/sec
o3 Pro
4 tokens/sec
GPT-4.1 Pro
38 tokens/sec
DeepSeek R1 via Azure Pro
28 tokens/sec
2000 character limit reached

Motion Blur Decomposition with Cross-shutter Guidance (2404.01120v1)

Published 1 Apr 2024 in cs.CV

Abstract: Motion blur is a frequently observed image artifact, especially under insufficient illumination where exposure time has to be prolonged so as to collect more photons for a bright enough image. Rather than simply removing such blurring effects, recent researches have aimed at decomposing a blurry image into multiple sharp images with spatial and temporal coherence. Since motion blur decomposition itself is highly ambiguous, priors from neighbouring frames or human annotation are usually needed for motion disambiguation. In this paper, inspired by the complementary exposure characteristics of a global shutter (GS) camera and a rolling shutter (RS) camera, we propose to utilize the ordered scanline-wise delay in a rolling shutter image to robustify motion decomposition of a single blurry image. To evaluate this novel dual imaging setting, we construct a triaxial system to collect realistic data, as well as a deep network architecture that explicitly addresses temporal and contextual information through reciprocal branches for cross-shutter motion blur decomposition. Experiment results have verified the effectiveness of our proposed algorithm, as well as the validity of our dual imaging setting.

Definition Search Book Streamline Icon: https://streamlinehq.com
References (38)
  1. From two rolling shutters to one global shutter. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 2505–2513, 2020.
  2. Restoration of video frames from a single blurred image with motion understanding. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 701–710, 2021.
  3. Rethinking coarse-to-fine approach in single image deblurring. In Proceedings of the IEEE/CVF international conference on computer vision, pages 4641–4650, 2021.
  4. Inverting a rolling shutter camera: bring rolling shutter images to high framerate global shutter video. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 4228–4237, 2021.
  5. Sunet: symmetric undistortion network for rolling shutter correction. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 4541–4550, 2021.
  6. Context-aware video reconstruction for rolling shutter cameras. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 17572–17582, 2022.
  7. Dynamic scene deblurring with parameter selective sharing and nested skip connections. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 3848–3856, 2019.
  8. Real-time intermediate flow estimation for video frame interpolation. In Computer Vision–ECCV 2022: 17th European Conference, Tel Aviv, Israel, October 23–27, 2022, Proceedings, Part XIV, pages 624–642. Springer, 2022.
  9. Single image deblurring with row-dependent blur magnitude. In Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), pages 12269–12280, 2023a.
  10. Rethinking video frame interpolation from shutter mode induced degradation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 12259–12268, 2023b.
  11. Learning to extract a video sequence from a single motion-blurred image. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pages 6334–6342, 2018.
  12. Learning to extract flawless slow motion from blurry videos. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 8112–8121, 2019.
  13. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980, 2014.
  14. Deep shutter unrolling network. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 5941–5949, 2020.
  15. Llnet: A deep autoencoder approach to natural low-light image enhancement. Pattern Recognition, 61:650–662, 2017.
  16. Mbllen: Low-light image/video enhancement using cnns. In BMVC, page 4, 2018.
  17. Deep multi-scale convolutional neural network for dynamic scene deblurring. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 3883–3891, 2017.
  18. Ntire 2019 challenge on video deblurring and super-resolution: Dataset and study. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, pages 0–0, 2019.
  19. Demfi: deep joint deblurring and multi-frame interpolation with flow-guided attentive correlation and recursive boosting. In Computer Vision–ECCV 2022: 17th European Conference, Tel Aviv, Israel, October 23–27, 2022, Proceedings, Part VII, pages 198–215. Springer, 2022.
  20. Multi-temporal recurrent neural networks for progressive non-uniform single image deblurring with incremental temporal training. In Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part VI 16, pages 327–343. Springer, 2020.
  21. Region-adaptive dense network for efficient motion deblurring. arxiv eprints, page. arXiv preprint arXiv:1903.11394, 2(5), 2019.
  22. Bringing alive blurred moments. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 6830–6839, 2019.
  23. Photosequencing of motion blur using short and long exposures. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, pages 510–511, 2020.
  24. Real-world blur dataset for learning and benchmarking deblurring algorithms. In Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXV 16, pages 184–201. Springer, 2020.
  25. Dual-shutter optical vibration sensing. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 16324–16333, 2022.
  26. Blurry video frame interpolation. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 5114–5123, 2020.
  27. Event-based fusion for motion deblurring with cross-modal attention. In Computer Vision–ECCV 2022: 17th European Conference, Tel Aviv, Israel, October 23–27, 2022, Proceedings, Part XVIII, pages 412–428. Springer, 2022.
  28. Scale-recurrent network for deep image deblurring. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 8174–8182, 2018.
  29. Uformer: A general u-shaped transformer for image restoration. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 17683–17693, 2022.
  30. Event-based blurry frame interpolation under blind exposure. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 1588–1598, 2023.
  31. Motion deblurring with real events. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 2583–2592, 2021.
  32. Video frame interpolation without temporal priors. Advances in Neural Information Processing Systems, 33:13308–13318, 2020.
  33. Towards rolling shutter correction and deblurring in dynamic scenes. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 9219–9228, 2021.
  34. Blur interpolation transformer for real-world motion from blur. arXiv preprint arXiv:2211.11423, 2022a.
  35. Bringing rolling shutter images alive with dual reversed distortion. In Computer Vision–ECCV 2022: 17th European Conference, Tel Aviv, Israel, October 23–27, 2022, Proceedings, Part VII, pages 233–249. Springer, 2022b.
  36. Animation from blur: Multi-modal blur decomposition with motion guidance. In Computer Vision–ECCV 2022: 17th European Conference, Tel Aviv, Israel, October 23–27, 2022, Proceedings, Part XIX, pages 599–615. Springer, 2022c.
  37. Evunroll: Neuromorphic events based rolling shutter image correction. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 17775–17784, 2022.
  38. Learning to reconstruct high speed and high dynamic range videos from events. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 2024–2033, 2021.

Summary

We haven't generated a summary for this paper yet.

Ai Sparkles Streamline Icon: https://streamlinehq.com Summarize Now
Github Logo Streamline Icon: https://streamlinehq.com

GitHub

  1. GitHub - jixiang2016/dualBR: [CVPR2024] Motion Blur Decomposition with Cross-shutter Guidance (6 stars)