Robust video denoising with sparse and dense noise modelings

Science China Information Sciences volume 61, Article number: 018103 (2018) Cite this article

This is a preview of subscription content, access via your institution.

Access options

Buy single article

Instant access to the full article PDF.

US$ 39.95

Tax calculation will be finalised during checkout.

References

  1. 1

    Boracchi G, Foi A, Egiazarian K. Video denoising using separable 4D nonlocal spatiotemporal transforms. Int Soc Opt Eng, 2011, 7870: 1–12

    Google Scholar 

  2. 2

    Sorel M, Bartos M. Fast Bayesian JPEG decompression and denoising with tight frame priors. IEEE Trans Image Process, 2016, 26: 490–501

    Article  Google Scholar 

  3. 3

    Ji H, Liu C Q, Shen Z W, et al. Robust video denoising using low rank matrix completion. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (CVPR), San Francisco, 2010. 1791–1798

    Google Scholar 

  4. 4

    Meng D Y, Fernando D L T. Robust matrix factorization with unknown noise. In: Proceedings of IEEE International Conference on Computer Vision, Sydney, 2013. 1337–1344

    Google Scholar 

  5. 5

    Cao X Y, Chen Y, Zhao Q, et al. Low-rank matrix factorization under general mixture noise distributions. In: Proceedings of IEEE International Conference on Computer Vision, Santiago, 2015. 1493–1501

    Google Scholar 

  6. 6

    Wright J, Peng Y G, Ma Y, et al. Robust principal component analysis: exact recovery of corrupted low-rank matrices by convex optimization. In: Proceedings of the 22nd International Conference on Neural Information Processing Systems, Vancouver, 2009. 2080–2088

    Google Scholar 

  7. 7

    Okutomi M, Yan S, Sugimoto S, et al. Practical lowrank matrix approximation under robust L1-norm. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Providence, 2012. 1410–1417

    Google Scholar 

  8. 8

    Shen G P, Han Z, Tang Y D. Robust video denoising by low-rank decomposition and modeling noises with mixture of Gaussian. In: Proceedings of IEEE International Conference on Robotics and Biomimetics (ROBIO), Bali, 2014. 2226–2231

    Google Scholar 

  9. 9

    Gu S H, Zhang L, Zuo W M, et al. Weighted Nuclear Norm Minimization with Application to Image Denoising. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, Columbus, 2014. 2862–2869

    Google Scholar 

Download references

Acknowledgements

This work was supported by National Natural Science Foundation of China (Grant No. 61303168). The authors also thank the support by Youth Innovation Promotion Association CAS.

Author information

Affiliations

  1. State Key Laboratory of Robotics, Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang, 110016, China

    Guiping Shen, Zhi Han, Xi’Ai Chen & Yandong Tang

  2. University of Chinese Academy of Sciences, Beijing, 100049, China

    Guiping Shen & Xi’Ai Chen

Authors
  1. Guiping Shen
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Zhi Han
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Xi’Ai Chen
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Yandong Tang
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Zhi Han.

Electronic supplementary material

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Shen, G., Han, Z., Chen, X. et al. Robust video denoising with sparse and dense noise modelings. Sci. China Inf. Sci. 61, 018103 (2018). https://doi.org/10.1007/s11432-017-9200-6

Download citation