A hybrid defocused region segmentation approach using image matting

  • Benish Amin
  • Muhammad Mohsin Riaz
  • Abdul Ghafoor


In this paper, a hybrid defocused region segmentation using image matting is proposed. The technique incorporates three sharpness metrics which are magnitude spectrum slope, local total variation and local binary patterns to identify the in-focus pixels in the image. Trimap is generated automatically using sharpness maps to obtain the prior information and matting Laplacian is applied to propagate the trimap to the entire image based on color similarities. Simulation results compared using visual and quantitative metrics show the strength of the proposed technique.


Region segmentation Image matting Sharpness maps 


  1. Bae, S., & Durand, F. (2007). Defocus magnification. Computer Graphics Forum, 26, 571–579.CrossRefGoogle Scholar
  2. Bahrami, K., Kot, A. C., Fan, J. (2013). A novel approach for partial blur detection and segmentation. In IEEE International Conference on Multimedia and Expo, pp. 1–6.Google Scholar
  3. Chakrabarti, A., Zickler, T., & Freeman, W. T. (2010). Analyzing spatially varying blur. In IEEE International Conference on Computer Vision and Pattern Recognition, pp. 2512–2519.Google Scholar
  4. Chen, D. J., Chen, H. T., & Chang, L. W. (2016). Fast defocus map estimation. In International Conference on Image Processing, pp. 3091–3966.Google Scholar
  5. Couzinie-Devy, F., Sun, J., Alahari, K., & Ponce, J. (2013). Learning to estimate and remove non-uniform image blur. In IEEE International Conference on Computer Vision and Pattern Recognition, pp. 1075–1082.Google Scholar
  6. Field, D. J., & Brady, N. (1997). Visual sensitivity, blur and the sources of variability in the amplitude spectra of natural scenes. Vision Research, 37(23), 3367–3383.CrossRefGoogle Scholar
  7. Levin, A., Lischinski, D., & Weiss, Y. (2008). A closed-form solution to natural image matting. IEEE Transactions on Pattern Analysis and Machine Intelligence, 30(2), 228–242.CrossRefGoogle Scholar
  8. Liu, R., Li, Z., & Jia, J. (2008). Image partial blur detection and classification. In IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–8.Google Scholar
  9. Ma, K., Fu, H., Liu, T., Wang, Z., & Tao, D. (2016). Local blur mapping: Exploiting high-level semantics by deep neural networks. In IEEE Conference on Computer Vision and Pattern Recognition.Google Scholar
  10. Shi, J., Xu, L., & Jia, J. (2014a). Blur detection dataset.
  11. Shi, J., Xu, L., & Jia, J. (2014b). Discriminative blur detection features. In IEEE Conference on Computer Vision and Pattern Recognition, pp. 2965–2972.Google Scholar
  12. Shi, J., Xu, L., & Jia, J. (2015). Just noticeable defocus blur detection and estimation. In IEEE Conference on Computer Vision and Pattern Recognition, pp. 657–665.Google Scholar
  13. Su, B., Lu, S., & Tan, C. L. (2011). Blurred image region detection and classification. In ACM International Conference on Multimedia, pp. 1397–1400.Google Scholar
  14. Tang, C., Wu, J., Hou, Y., Wang, P., & Li, W. (2016). A spectral and spatial approach of coarse-to-fine blurred image region detection. IEEE Signal Processing Letters, 23(11), 1652–1656.CrossRefGoogle Scholar
  15. Thongkamwitoon, T., Muammar, H., & Dragotti, P.-L. (2015). An image recapture detection algorithm based on learning dictionaries of edge profiles. IEEE Transactions on Information Forensics and Security, 10(5), 953–968.CrossRefGoogle Scholar
  16. Vu, C. T., Phan, T. D., & Chandler, D. M. (2012). S3: A spectral and spatial measure of local perceived sharpness in natural images. IET Image Processing, 21(3), 934–945.CrossRefzbMATHGoogle Scholar
  17. Wang, J., & Cohen, M. (2005). An iterative optimization approach for unified image segmentation and matting. In International Conference on Computer Vision. pp. 17–21.Google Scholar
  18. Yang, D., & Qin, S. (2016). Restoration of partial blurred image based on blur detection and classification. Journal of Electrical and Computer Engineering, 2016(1), 1–12.MathSciNetGoogle Scholar
  19. Yi, X., & Eramian, M. (2016). LBP-based segmentation of defocus blur. IEEE Transactions on Image Processing, 25(4), 1626–1638.MathSciNetCrossRefGoogle Scholar
  20. Zhang, X., Wang, R., Jiang, X., Wang, W., & Gao, W. (2016a). Spatially variant defocus blur map estimation and deblurring from a single image. Journal of Visual Communication and Image Representation, 35, 257–264.CrossRefGoogle Scholar
  21. Zhang, L., & Zhang, D. (2016a). Robust visual knowledge transfer via extreme learning machine-based domain adaptation. IEEE Transactions on Image Processing, 25(10), 4959–4973.MathSciNetCrossRefGoogle Scholar
  22. Zhang, L., & Zhang, D. (2016b). Evolutionary cost-sensitive extreme learning machine. IEEE Transactions on Neural Networks and Learning Systems, PP(99), 1–6.Google Scholar
  23. Zhang, L., & Zhang, D. (2016c). Visual understanding via multi-feature shared learning with global consistency. IEEE Transactions on Multimedia, 18(2), 247–259.CrossRefGoogle Scholar
  24. Zhang, L., Zuo, W., & Zhang, D. (2016b). LSDT: Latent sparse domain transfer learning for visual adaptation. IEEE Transactions on Image Processing, 25(3), 1177–1191.MathSciNetCrossRefGoogle Scholar
  25. Zhao, J., Feng, H., Xu, Z., Li, Q., & Tao, X. (2013). Automatic blur region segmentation approach using image matting. Signal, Image and Video Processing, 7(6), 1173–1181.CrossRefGoogle Scholar
  26. Zhu, X., Cohen, S., Schiller, S., & Milanfar, P. (2013). Estimating spatially varying defocus blur from a single image. IEEE Transactions on Image Processing, 22(12), 4879–4891.MathSciNetCrossRefzbMATHGoogle Scholar
  27. Zhuo, S., & Sim, T. (2011). Defocus map estimation from a single image. Pattern Recognition, 44(9), 1852–1858.CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  1. 1.National University of Sciences and Technology (NUST)IslamabadPakistan
  2. 2.Center for Advanced Studies in Telecommunication (CAST), COMSATSIslamabadPakistan

Personalised recommendations