Skip to main content
SpringerLink
Log in

A novel denoising method for infrared image based on bilateral filtering and non-local means

  • Published:
Optoelectronics Letters Aims and scope Submit manuscript

Abstract

This paper presents an image denoising method based on bilateral filtering and non-local means. The non-local region texture or structure of the image has the characteristics of repetition, which can be used to effectively preserve the edge and detail of the image. And compared with classical methods, bilateral filtering method has a better performance in denosing for the reason that the weight includes the geometric closeness factor and the intensity similarity factor. We combine the geometric closeness factor with the weight of non-local means, and construct a new weight. Experimental results show that the modified algorithm can achieve better performance. And it can protect the image detail and structure information better.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

References

  1. Yoshizawa Shin, Belyaev Alexander and Yokota Hideo, Computer Graphics Forum 29, 60 (2010).

    Article  Google Scholar 

  2. Li Ying Jiang, Yan Li and Yang Bo, Open Automation and Control Systems Journal 7, 275 (2015).

    Article  Google Scholar 

  3. Mairal Julien, Bach Francis, Ponce Jean, Sapiro Guillermo and Zisserman Andrew, Non-Local Sparse Models for Image Restoration, IEEE International Conference on Computer Vision 30, 2272 (2009).

    Google Scholar 

  4. Dabov Kostadin, Foi Alessandro, Katkovnik Vladimir and Egiazarian Karen, IEEE Transactions on Image Processing 16, 2080 (2007).

    Article  ADS  MathSciNet  Google Scholar 

  5. LI Zheng, LIU Wen-jiang, RONG Meng-tian and LIU Tai-zhi, Information Technology 4, 30 (2012). (in Chinese)

    Google Scholar 

  6. Danielyan Aram, Katkovnik Vladimir and Egiazarian Karen, IEEE Transactions on Image Processing 21, 1715 (2012).

    Article  ADS  MathSciNet  Google Scholar 

  7. Dai Li, Zhang Yousai and Li Yuanjiang, International Journal of Signal Processing, Image Processing and Pattern Recognition 6, 41 (2013).

    Article  Google Scholar 

  8. Luo Xue-Gang, LÜ Jun-Rui, Wang Hua-Jun and Yang Qiang, Journal of the University of Electronic Science and Technology of China 44, 84 (2015). (in Chinese)

    Google Scholar 

  9. Thaipanich Tanaphol, Oh Byung Tae, Wu Ping-Hao, Xu Daru and Kuo C.-C. Jay, IEEE Transactions on Consumer Electronics 56, 2623 (2010).

    Article  Google Scholar 

  10. Wu Yiquan, Dai Yimian, Yin Jun and Wu Jiansheng, Transactions of Tianjin University 21, 104 (2015).

    Article  Google Scholar 

  11. B. K. Shreyamsha Kumar, Signal, Image and Video Processing 7, 1211 (2013).

    Article  Google Scholar 

  12. Gan Kaihua, Tan Jieqing and He Lei, Non-Local Means Image Denoising Algorithm Based on Edge Detection, 5th International Conference on Digital Home, 117 (2014).

    Google Scholar 

  13. Gao Zong-li, Ye Wei-lin, Zheng Chuan-tao and Wang Yi-ding, Optoelectronics Letters 10, 299 (2014).

    Article  ADS  Google Scholar 

  14. Zhao De-xin, Liu Peng-jie and Zhang De-gan, Optoelectronics Letters 10, 477 (2014).

    Article  ADS  Google Scholar 

  15. Kizilkaya Aydin and Elbi Mehmet Dogan, IETE Journal of Research 62, 605 (2016).

    Article  Google Scholar 

  16. Ikemoto Yusuke and Sekiyama Kosuke, Journal of Ad vanced Computational Intelligence and Intelligent Informatics 20, 705 (2016).

    Article  Google Scholar 

  17. Jonatas Lopes de Paiva, Claudio F. M. Toledo and Helio Pedrini, Applied Soft Computing 46, 778 (2016).

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Key Laboratory of Computer Vision and System, Ministry of Education of China, Tianjin Key Laboratory of Intelligence Computing and Novel Software Technology, School of Computer and Communication Engineering, Tianjin University of Technology, Tianjin, 300384, China

    Feng-lian Liu  (刘凤连), Meng-yao Sun  (孙梦尧) & Wen-na Cai  (蔡文娜)

Authors
  1. Feng-lian Liu  (刘凤连)
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Meng-yao Sun  (孙梦尧)
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Wen-na Cai  (蔡文娜)
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Feng-lian Liu  (刘凤连).

Additional information

This work has been supported by the Student’s Platform for Innovation and Entrepreneurship Training Program (No.201510060022).

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Liu, Fl., Sun, My. & Cai, Wn. A novel denoising method for infrared image based on bilateral filtering and non-local means. Optoelectron. Lett. 13, 237–240 (2017). https://doi.org/10.1007/s11801-017-7007-8

Download citation

  • Received:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11801-017-7007-8

Search

Navigation