Log in

A robust auto-focus measure based on inner energy

  • Published:
Optoelectronics Letters Aims and scope Submit manuscript

Abstract

This paper proposes a robust auto-focus (AF) measure based on inner energy. In general, the inner energy of noise pixels is close to zero because the magnitude of gradient and the direction of the noise pixels are random. Therefore, the inner energy can effectively eliminate the influence of noise on image quality assessment. But the gradients of near edge points are consistent with those of edge points, so the inner energy of edge pixels is relatively large, and the detail information of the image can be highlighted. Experimental results indicate that compared with traditional methods, the proposed method has higher accuracy, fewer local peaks, stronger robustness and better practicability. In particular, the evaluation results are close to the subjective evaluation of the human eyes. These results illustrate that the proposed method can be applied in automatic focusing.

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

Access this article

Subscribe and save

Springer+ Basic
EUR 32.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or Ebook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price includes VAT (United Kingdom)

Instant access to the full article PDF.

Similar content being viewed by others

References

  1. Han J W, Kim J H, Lee H T and Ko S J, IEEE Transactions on Consumer Electronics 57, 232 (2011).

    Article  Google Scholar 

  2. Lee S Y, Kumar Y, Cho J M, Lee S W and Kim S W, IEEE Transactions on Circuits & Systems for Video Technology 18, 1237 (2008).

    Article  Google Scholar 

  3. Pertuz S, Puig D and Garcia M A, Pattern Recognition 46, 1157 (2012).

    Google Scholar 

  4. J. Jeon, J. Lee and J. Paik, IEEE Transactions Consumer Electronics 57, 1 (2011).

    Article  Google Scholar 

  5. Zhang X, Wu H and Ma Y, Applied & Computational Harmonic Analysis 40, 430 (2015).

    Article  Google Scholar 

  6. Yang G and Nelson B J, Wavelet-Based Autofocusing and Unsupervised segmentation of Microscopic Images, International IEEE Conference on Intelligent Robots and Systems 3, 2143 (2003).

    Google Scholar 

  7. Huang J T, Shen C H, Phoong S M and Chen H, Robust Measure of Image Focus in the Wavelet Domain, IEEE International Symposium on Intelligent Signal Processing and Communication Systems, 212 (2005).

  8. Kong L J and Nie P, Journal of Optoelectronics·Laser 27, 198 (2016). (in Chinese)

    Google Scholar 

  9. LV H X, Zhao Z G, Guo Y J and Wang F C, Journal of Optoelectronics·Laser 27, 77 (2016). (in Chinese)

    Google Scholar 

  10. Donoho D L, IEEE Transactions on Information Theory 41, 613 (1995).

    Article  MathSciNet  Google Scholar 

  11. Gonzalez R C, Woods R E and Masters B R, Digital Image Processing, Third Edition, Pientice Hall, (2008).

    Google Scholar 

  12. Wang Z H and Wu F C, Chinese Journal of Computers 32, 2211 (2009). (in Chinese)

    ADS  Google Scholar 

  13. Subbarao M and Tyan J K, Optimal Focus Measure for Passive Autofocusing and Depth-from-Focus, Symposium on Videometrics IV, 89 (1999).

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Wei Huang  (黄薇).

Additional information

This work has been supported by the National Natural Science Foundation of China (Nos.U1509207 and 61325019).

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Li, Y., Tang, Tl. & Huang, W. A robust auto-focus measure based on inner energy. Optoelectron. Lett. 13, 309–313 (2017). https://doi.org/10.1007/s11801-017-7052-3

Download citation

  • Received:

  • Revised:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11801-017-7052-3

Navigation