Papers
Topics
Authors
Recent
Gemini 2.5 Flash
Gemini 2.5 Flash
194 tokens/sec
GPT-4o
7 tokens/sec
Gemini 2.5 Pro Pro
45 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

Unsupervised Coordinate-Based Video Denoising (2307.00179v1)

Published 1 Jul 2023 in cs.CV and eess.IV

Abstract: In this paper, we introduce a novel unsupervised video denoising deep learning approach that can help to mitigate data scarcity issues and shows robustness against different noise patterns, enhancing its broad applicability. Our method comprises three modules: a Feature generator creating features maps, a Denoise-Net generating denoised but slightly blurry reference frames, and a Refine-Net re-introducing high-frequency details. By leveraging the coordinate-based network, we can greatly simplify the network structure while preserving high-frequency details in the denoised video frames. Extensive experiments on both simulated and real-captured demonstrate that our method can effectively denoise real-world calcium imaging video sequences without prior knowledge of noise models and data augmentation during training.

Definition Search Book Streamline Icon: https://streamlinehq.com
References (28)
  1. Mip-nerf: A multiscale representation for anti-aliasing neural radiance fields. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 5855–5864, 2021.
  2. Implicit functions in feature space for 3d shape reconstruction and completion. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 6970–6981, 2020.
  3. Videnn: Deep blind video denoising. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition workshops, pages 0–0, 2019.
  4. Self-supervised training for blind multi-frame video denoising. In Proceedings of the IEEE/CVF winter conference on applications of computer vision, pages 2724–2734, 2021.
  5. Equivariant neural rendering. In International Conference on Machine Learning, pages 2761–2770. PMLR, 2020.
  6. Model-blind video denoising via frame-to-frame training. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 11369–11378, 2019.
  7. Deep residual learning for image recognition. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 770–778, 2016.
  8. Batch normalization: Accelerating deep network training by reducing internal covariate shift. In International conference on machine learning, pages 448–456. pmlr, 2015.
  9. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980, 2014.
  10. Noise2void-learning denoising from single noisy images. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 2129–2137, 2019.
  11. High-quality self-supervised deep image denoising. Advances in Neural Information Processing Systems, 32, 2019.
  12. Removing independent noise in systems neuroscience data using deepinterpolation. Nature methods, 18(11):1401–1408, 2021.
  13. Noise2noise: Learning image restoration without clean data. arXiv preprint arXiv:1803.04189, 2018.
  14. Recurrent video restoration transformer with guided deformable attention. Advances in Neural Information Processing Systems, 35:378–393, 2022.
  15. Nerf in the dark: High dynamic range view synthesis from noisy raw images. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 16190–16199, 2022.
  16. Nerf: Representing scenes as neural radiance fields for view synthesis. Communications of the ACM, 65(1):99–106, 2021.
  17. The 2017 davis challenge on video object segmentation. arXiv preprint arXiv:1704.00675, 2017.
  18. Free view synthesis. In Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XIX 16, pages 623–640. Springer, 2020.
  19. Unsupervised deep video denoising. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 1759–1768, 2021.
  20. Implicit neural representations with periodic activation functions. Advances in Neural Information Processing Systems, 33:7462–7473, 2020.
  21. Neural geometric level of detail: Real-time rendering with implicit 3d shapes. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 11358–11367, 2021.
  22. Fourier features let networks learn high frequency functions in low dimensional domains. Advances in Neural Information Processing Systems, 33:7537–7547, 2020.
  23. Dvdnet: A fast network for deep video denoising. In 2019 IEEE International Conference on Image Processing (ICIP), pages 1805–1809. IEEE, 2019.
  24. Fastdvdnet: Towards real-time deep video denoising without flow estimation. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 1354–1363, 2020.
  25. Multi-scale context aggregation by dilated convolutions. arXiv preprint arXiv:1511.07122, 2015.
  26. Supervised raw video denoising with a benchmark dataset on dynamic scenes. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 2301–2310, 2020.
  27. Beyond a gaussian denoiser: Residual learning of deep cnn for image denoising. IEEE transactions on image processing, 26(7):3142–3155, 2017.
  28. Ffdnet: Toward a fast and flexible solution for cnn-based image denoising. IEEE Transactions on Image Processing, 27(9):4608–4622, 2018.

Summary

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

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