A Hybrid Approach for Blur Detection Using Naïve Bayes Nearest Neighbor Classifier

Автор: Harjot Kaur, Mandeep Kaur

Журнал: International Journal of Information Technology and Computer Science(IJITCS) @ijitcs

Статья в выпуске: 12 Vol. 8, 2016 года.

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Blur detection of the partially blurred image is challenging because in this case blur varies spatially. In this paper, we propose a blurred-image detection framework for automaticallQy detecting blurred and non-blurred regions of the image. We propose a new feature vector that consists of the information of an image patch as well as blur kernel. That is why it is called kernel-specific feature vector. The information extracted about an image patch is based on blurred pixel behavior on local power spectrum slope, gradient histogram span, and maximum saturation methods. To make the features vector useful for real applications, kernels consisting of motion-blur kernels, defocus-blur kernels, and their combinations are used. Gaussian filters are used for filtering process of extracted features and kernels. Construction of kernel-specific feature vector is followed by the proposed Naïve Bayes Classifier based on Nearest Neighbor classification method (NBNN). The proposed algorithm outperforms the up-to-date blur detection method. Because blur detection is an initial step for the de-blurring process of partially blurred images, our results also demonstrate the effectiveness of the proposed method in deblurring process.

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Blur detection, feature extraction, motion blur, defocus blur, support vector machine (SVM), NBNN, deblurring

Короткий адрес: https://sciup.org/15012602

IDR: 15012602

Список литературы A Hybrid Approach for Blur Detection Using Naïve Bayes Nearest Neighbor Classifier

  • Bovik and J. Gibson, Handbook of image and video processing, Academic Press, Inc. Orlando, FL, USA, 2000.
  • Y. Chung, J. Wang, R. Bailey, S. Chen and S. Chang, “A nonparametric blur measure based on edge analysis for image processing applications”, IEEE Conference on Cybernetics and Intelligent Systems, vol. 1, 356 – 360, 2004.
  • J. H. Elder and S. W. Zucker, “Local scale control for edge detection and blur estimation,” IEEE Conf. on Pattern Analysis and Machine Intelligence, vol. 20, no. 7, pp. 699–716, 1998.
  • W. T. Freeman and E. H. Adelson, “The design and use of steerable filters,” IEEE Conf. on Pattern Analysis and Machine Intelligence (PAMI), vol. 13, no. 9, pp. 891–906, 1991.
  • W. Zhang and F. Bergholm, “Multi-scale blur estimation and edge type classification for scene analysis,” International Journal of Computer Vision, vol. 24, no. 3, pp. 219–250, 1997.
  • Jaeseung Ko and Changick Kim, “Low cost blur image detection and estimation for mobile devices,” IEEE International Conference on Advanced Computing Technologies (ICACT), vol. 3, pp. 1605-1610, 2009.
  • J. Shi, L. Xu. and J. Jia, “Discriminative blur detection features,” IEEE Int. Conf. Comput. Vis. Pattern Recognit., pp. 2965-2972, 2014.
  • R. Liu, Z. Li and J. Jia, “Image partial blur detection and classification,” CVPR, pp. 1-8, 2008.
  • Liu Debing, Chen Zhibo, Ma Huadong, Xu Feng and Gu Xiaodong, “No reference block based blur detection,” Quality of Multimedia Experience International Workshop, pp. 75-80, 2009.
  • Niranjan D. Narvekar and Lina J. Karam, “A non-reference image blur metric based on the cumulative probability of blur detection (CPBD),” IEEE Trans. on Image Processing, pp. 2678-2683, 2011.
  • Xiaogang Chen, Jie Yang, Qiang Wu and Jiajia Zhao, “Motion blur detection based on lowest directional high-frequency energy,” IEEE International Conference on Image Processing (ICIP), pp. 2533-2536, 2010.
  • X. Zhu, S. Cohen, S. Schiller and P. Milanfar X. Zhu, “Estimating spatially varying defocus blur from single image,” IEEE Trans. on Image Process., pp. 4879-4891, 2013.
  • V. Kanchev, K. Tonchev and O. Boumbarov, “Blurred image regions detection using wavelet-based histograms and SVM,” IEEE International Conference on Intelligent Data Acquisition and Advanced Computing Systems (IDAACS), pp. 457-461, 2011.
  • Y. Pang, H. Zhu, Xinyu Li and Xuelong li, “Classifying discriminative features for blur detection,” IEEE Trans. on Cybernetics, pp. 2168-2267, 2015.
  • C. S. Won, K. Pyun and R. M. Gray, “Automatic object segmentation in images with low depth of field,” ICIP, 3, pp. 805-808, 2002.
  • C. Kim, “Segmenting a Low-Depth-of-Field image using morphological filters and region mergin,” IEEE Transactions on Image Processing, no. 14, pp. 1503-1511, 2005.
  • L. Kovacs and T. Sziranyi, “Focus area extraction by blind deconvolution for defining regions of interest,” PAMI, vol. 29, no. 6, pp. 1080-1085, 2007.
  • J. Z. Wang, J. Li, R. M. Gray and G. Wiederhold, “Unsupervised multiresolution segmentation for images with low depth of field,” PAMI, 23, pp. 85, 2001.
  • R. Datta, D. Joshi, J. Li and J. Z. Wang, “Studying aesthetics in photographic images using a computational approach,” ECCV , pp. 288-301, 2006.
  • A. Chakrabarti, T. Zickler, and W.T. Freeman, “Analyzing spatially-varying blur,” IEEE Int. Conf. on Comput. Vis. Pattern Recognit., pp. 2512-2519, 2010.
  • M. Banham and A. Katsaggelos, “Digital image restoration,” IEEE Signal Processing Magazine, pp. 24-41, 1997.
  • R. Lagendijk, A. Katsaggelos and J. Biemond “Iterative identification and restoration of image,” International Conference on Acoustics, Speech, and Signal Processing, pp. 992-995, 1998.
  • D. Kundur and D. Hatzinakos, “Blind image deconvolution,” IEEE Signal Processing Magazine, vol. 13, no. 3, pp. 43 – 64, 1996.
  • G. Pavlovic and M. Tekalp, “Maximum likelihood parametric blur identification based on a continuous spatial domain model,” IEEE Trans. on Image Processing, vol. 1, no. 4, pp. 496-504, 1992.
  • J. Jia, “Single image motion deblurring using transparency,” IEEE Int. Conf. on Comput. Vis. and Pattern Recognition, pp. 1-8, 2007.
  • L. Bar, B.Berkels, M.Rumpf and G. Sapiro, “A variational framework for simaltaneous motion estimation and restoration of motion-blurred video,” IEEE Int. Conf. on Comput. Vis., pp. 1-8, 2007.
  • B. Su., S. Lu., and C.L. Tan, "Blurred image region detection and classification," ACM Int. Conf. Multimedia, pp. 1397-1400, 2011.
  • Elena Lazkano and Basilio Sierra, Progress in Artificial intelligence, Springer Berlin Heidelberg, vol. 2902, pp. 171-183, 2003.
  • Blur Detection Dataset (2015) Available online at: http://www.cse.cuhk.edu.hk/~leojia/projects/dblurdetect/dataset.html.
  • X. Lu, X.Li. and L.Mou, “Semi-Supervised multitask learning for scene recognition,” IEEE Trans. Cybernetics, vol. 45, no. 9, pp. 1967-1976, 2015. Y. Pang., K. Wang, Y. Yuan and K. Zhang, “Distributed object detection with linear SVM,” IEEE Trans. Cybernetics, vol. 44, no. 11, pp. 2122-2133, 2014.
  • Regis Behmo, Arnak Dalalyan, Paul Marcombes and Veronique Prinet, “ Towards optimal naïve bayes nearest neighbor,” Springer Berlin Heidelberg, pp. 171-184, 2010.
  • Sancho McCann and David G.Lowe, “Local naive bayes nearest neighbor for image classification,”IEEE Conference on Comp. Vision and Pattern Recognition, pp. 3650-3656, 2012.
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