Signal, Image and Video Processing

, Volume 11, Issue 1, pp 163–170 | Cite as

Sharpness estimation in facial images by spectrum approximation

Original Paper
First Online:
Received:
Revised:
Accepted:
  • 234 Downloads

Abstract

This paper presents a novel approach to image sharpness assessment designed primarily for facial images. The approach can be described as holistic analysis of the frequency–amplitude spectrum by means of fitting an approximation model and obtaining the estimate based on the model’s parameters. The proposed method shows better correlation with perceived sharpness than other existing methods both on synthetic tests and on a set of real-world face images. We demonstrate an application of the resulting estimate to enhance the accuracy of a face gender classifier.

Keywords

Blur detection Frequency-domain analysis Image quality assessment No-reference metric Sharpness estimation 
This is a preview of subscription content, log in to check access.

Supplementary material

11760_2016_915_MOESM1_ESM.ipynb (109 kb)
Supplementary material 1 (ipynb 108 KB)
11760_2016_915_MOESM2_ESM.html (435 kb)
Supplementary material 2 (html 435 KB)
11760_2016_915_MOESM3_ESM.txt (1 kb)
Supplementary material 3 (txt 1 KB)
11760_2016_915_MOESM4_ESM.tar (580 kb)
Supplementary material 4 (tar 580 KB)

References

  1. 1.
    Ferzli, R., Karam, L.J.: A no-reference objective image sharpness metric based on the notion of just noticeable blur (JNB). IEEE Trans. Image Process. 18(4), 717–728 (2009)MathSciNetCrossRefGoogle Scholar
  2. 2.
    Gaidhane, V.H., Hote, Y.V., Singh, V.: Image focus measure based on polynomial coefficients and spectral radius. SIViP 9(1), 203–211 (2015)CrossRefGoogle Scholar
  3. 3.
    Manap, R., Shao, L.: Non-distortion-specific no-reference image quality assessment: a survey. Inf. Sci. 301, 141–160 (2015)CrossRefGoogle Scholar
  4. 4.
    Minin, P., Mikhaylov, D.: Analysis of gender recognition methods’ robustness. In: Proceedings of Fifth International Conference on Intelligent Control and Information Processing, Dalian (2014)Google Scholar
  5. 5.
    Batten, C.F.: Autofocusing and Astigmatism Correction in the Scanning Electron Microscope (2000)Google Scholar
  6. 6.
    Crété-Roffet, F., Dolmiere, T., Ladret, P., Nicolas, M.: The blur effect: perception and estimation with a new no-reference perceptual blur metric. In: SPIE Electronic Imaging Symposium Conference on Human Vision and Electronic Imaging, San Jose, USA (2007)Google Scholar
  7. 7.
    Gu, K., Zhai, G., Lin, W., Yang, X., Zhang, W.: No-reference image sharpness assessment in autoregressive parameter space. IEEE Trans. Image Process. 24(10), 3218–3231 (2015)MathSciNetCrossRefGoogle Scholar
  8. 8.
    Bahrami, K., Kot, A.: A fast approach for no-reference image sharpness assessment based on maximum local variation. IEEE Signal Process. Lett. 21(6), 751–755 (2014)CrossRefGoogle Scholar
  9. 9.
    Zhu, X., Milanfar, P.: A no-reference sharpness metric sensitive to blur and noise. In: International Workshop on Quality of Multimedia Experience, 2009. QoMEx 2009 (2009)Google Scholar
  10. 10.
    Abdalmajeed, S., Shuhong, J.: Using the natural scenes’ edges for assessing image quality blindly and efficiently. Math. Probl. Eng. 2015, 9 (2015). doi: 10.1155/2015/389504
  11. 11.
    Li, L., Lin, W., Wang, X., Yang, G., Bahrami, K., Kot, A.: No-reference image blur assessment based on discrete orthogonal moments. IEEE Trans. Cybern. 46(1), 39–50 (2016)CrossRefGoogle Scholar
  12. 12.
    Kang, L., Ye, P., Li, Y., Doermann, D.: Convolutional Neural Networks for No-Reference Image Quality Assessment. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1733–1740 (2014)Google Scholar
  13. 13.
    Marziliano, P., Dufaux, F., Winkler, S., Ebrahimi, T.: A no-reference perceptual blur metric. In: Proceedings of the International Conference on Image Processing, Rochester, NY (2002)Google Scholar
  14. 14.
    Guan, J., Zhang, W., Gu, J., Ren, H.: No-reference blur assessment based on edge modeling. J. Vis. Commun. Image Represent. 29, 1–7 (2015)CrossRefGoogle Scholar
  15. 15.
    De, K., Masilamani, V.: Image sharpness measure for blurred images in frequency domain. Procedia Eng. 64, 149–158 (2013)CrossRefGoogle Scholar
  16. 16.
    Cohen, E., Yitzhaky, Y.: No-reference assessment of blur and noise impacts on image quality. Signal Image Video Process. 4(3), 289–302 (2010)CrossRefGoogle Scholar
  17. 17.
    Vu, C.T., Phan, T.D., Chandler, D.M.: S3: a spectral and spatial measure of local perceived sharpness in natural images. IEEE Trans. Image Process. 21(3), 934–945 (2012)MathSciNetCrossRefGoogle Scholar
  18. 18.
    Ciancio, A., da Costa, A.T., da Silva, E.A., Said, A., Samadani, R., Obrador, P.: No-reference blur assessment of digital pictures based on multifeature classifiers. IEEE Trans. Image Process. 20(1), 64–75 (2011)MathSciNetCrossRefGoogle Scholar
  19. 19.
    Harris, F.J.: On the use of windows for harmonic analysis with the discrete Fourier transform. Proc. IEEE 66(1), 51–83 (1978)CrossRefGoogle Scholar
  20. 20.
    Field, D.J., Brady, N.: Visual sensitivity, blur and the sources of variability in the amplitude spectra of natural scenes. Vis. Res. 37(23), 3367–3383 (1997)CrossRefGoogle Scholar
  21. 21.
    Sheikh, H.R., Sabir, M.F., Bovik, A.C.: A statistical evaluation of recent full reference image quality assessment algorithms. IEEE Trans. Image Process. 15(11), 3440–3451 (2006)CrossRefGoogle Scholar
  22. 22.
    Jones, E., Oliphant, T., Peterson, P., et al.: SciPy: Open source scientific tools for Python. http://www.scipy.org/ (2001). Accessed 16 June 2016
  23. 23.
    Storn, R., Price, K.: Differential evolution-a simple and efficient heuristic for global optimization over continuous spaces. J. Glob. Optim. 11(4), 341–359 (1997)MathSciNetCrossRefMATHGoogle Scholar
  24. 24.
    Nordstrøm, M.M., Larsen, M., Sierakowski, J., Stegmann, M.B.: The IMM Face Database—An Annotated Dataset of 240 Face Images. Informatics and Mathematical Modelling, Technical University of Denmark, DTU (2004)Google Scholar
  25. 25.
    Huang, G.B., Mattar, M., Berg, T., Learned-Miller, E.: Labeled faces in the wild: a database for studying face recognition in unconstrained environments. In: Workshop on Faces in ’Real-Life’ Images: Detection, Alignment, and Recognition (2008)Google Scholar
  26. 26.
    Cortes, C., Vapnik, V.: Support-vector networks. Mach. Learn. 20, 273–297 (1995)MATHGoogle Scholar
  27. 27.
    Pinto, N., Cox, D.D., DiCarlo, J.J.: Why is real-world visual object recognition hard? PLoS Comput. Biol. 4, e27 (2008)MathSciNetCrossRefGoogle Scholar
  28. 28.
    Shan, C.: Learning local binary patterns for gender classification on real-world face images. Pattern Recogn. Lett. 33, 431–437 (2012)CrossRefGoogle Scholar
  29. 29.
    Wolf, L., Hassner, T., Taigman, Y.: Descriptor based methods in thewild. In: Workshop on Faces in “Real-Life” Images: Detection, Alignment, and Recognition (2008)Google Scholar
  30. 30.
    Turk, M., Pentland, A.: Eigenfaces for recognition. J.Cogn. Neurosci. 3, 71–86 (1991)CrossRefGoogle Scholar
  31. 31.
    Fiche, C., Ladret, P., Vu, N.-S.: Blurred face recognition algorithm guided by a no-reference blur metric. In: IS&T/SPIE Electronic Imaging (2010)Google Scholar

Copyright information

© Springer-Verlag London 2016

Authors and Affiliations

  1. 1.National Research Nuclear University MEPhIMoscowRussia
  2. 2.Bauman Moscow State Technical UniversityMoscowRussia

Personalised recommendations