Blur Detection through Multinomial Logistic Regression based Adaptive Threshold

  • Mahmood, Muhammad Tariq (Korea University of Technology and Education, School of Computer Science and Engineering) ;
  • Siddiqui, Shahbaz Ahmed (Korea University of Technology and Education, School of Computer Science and Engineering) ;
  • Choi, Young Kyu (Korea University of Technology and Education, School of Computer Science and Engineering)
  • Received : 2019.12.13
  • Accepted : 2019.12.16
  • Published : 2019.12.31

Abstract

Blur detection and segmentation play vital role in many computer vision applications. Among various methods, local binary pattern based methods provide reasonable blur detection results. However, in conventional local binary pattern based methods, the blur map is computed by using a fixed threshold irrespective of the type and level of blur. It may not be suitable for images with variations in imaging conditions and blur. In this paper we propose an effective method based on local binary pattern with adaptive threshold for blur detection. The adaptive threshold is computed based on the model learned through the multinomial logistic regression. The performance of the proposed method is evaluated using different datasets. The comparative analysis not only demonstrates the effectiveness of the proposed method but also exhibits it superiority over the existing methods.

Keywords

References

  1. Zhao, W., et al., Defocus Blur Detection via Multi-Stream Bottom-Top-Bottom Network. IEEE transactions on pattern analysis and machine intelligence, 2019.
  2. M. T. Mahmood and Y. K. Choi, "3D Surface Reconstruction by Combining Focus Measures through Genetic Algorithm," Journal of the Semiconductor & Display Technology, vol. 13, no. 2, pp. 27-32, 2014.
  3. M. T. Mahmood, U. Ali and Y. K. Choi, "Enhancing Focus Measurements in Shape From Focus through 3D Weighted Least Square," Journal of the Semiconductor & Display Technology, vol. 18, no. 3, pp. 66-71, 2019.
  4. Zhuo, S. and T. Sim, Defocus map estimation from a single image. Pattern Recognition, 2011. 44(9): p. 1852-1858. https://doi.org/10.1016/j.patcog.2011.03.009
  5. Shi, J., L. Xu, and J. Jia. Discriminative blur detection features. in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2014.
  6. Shi, J., L. Xu, and J. Jia. Just noticeable defocus blur detection and estimation. in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2015.
  7. Kim, B., et al. Defocus and Motion Blur Detection with Deep Contextual Features. in Computer Graphics Forum. 2018. Wiley Online Library.
  8. Yi, X. and M. Eramian, LBP-based segmentation of defocus blur. IEEE transactions on image processing, 2016. 25(4): p. 1626-1638. https://doi.org/10.1109/TIP.2016.2528042
  9. Golestaneh, S.A. and L.J. Karam. Spatially-Varying Blur Detection Based on Multiscale Fused and Sorted Transform Coefficients of Gradient Magnitudes. in CVPR. 2017.
  10. Bejczy, A., The 1986 IEEE international conference on robotics and automation-In retrospect. IEEE Journal on Robotics and Automation, 1987. 3(1): p. 84-84. https://doi.org/10.1109/JRA.1987.1087073
  11. Liu, R., Z. Li, and J. Jia. Image partial blur detection and classification. in 2008 IEEE conference on computer vision and pattern recognition. 2008. IEEE.
  12. Xiao, H., et al., Defocus blur detection based on multiscale SVD fusion in gradient domain. Journal of Visual Communication and Image Representation, 2019. 59: p. 52-61. https://doi.org/10.1016/j.jvcir.2018.12.048
  13. Zeng, K., et al., A Local Metric for Defocus Blur Detection Based on CNN Feature Learning. IEEE Transactions on Image Processing, 2018. 28(5): p. 2107-2115. https://doi.org/10.1109/TIP.2018.2881830