KSII Transactions on Internet and Information Systems (TIIS)

Korean Society for Internet Information (한국인터넷정보학회)
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Neural-network-based Impulse Noise Removal Using Group-based Weighted Couple Sparse Representation

  • Lee, Yongwoo (School of Electronic and Electrical Engineering, Sungkyunkwan University) ;
  • Bui, Toan Duc (School of Electronic and Electrical Engineering, Sungkyunkwan University) ;
  • Shin, Jitae (School of Electronic and Electrical Engineering, Sungkyunkwan University) ;
  • Oh, Byung Tae (School of Electronic, Korea Aerospace University)
  • Received : 2017.11.19
  • Accepted : 2018.02.24
  • Published : 2018.08.31
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Abstract

In this paper, we propose a novel method to recover images corrupted by impulse noise. The proposed method uses two stages: noise detection and filtering. In the first stage, we use pixel values, rank-ordered logarithmic difference values, and median values to train a neural-network-based impulse noise detector. After training, we apply the network to detect noisy pixels in images. In the next stage, we use group-based weighted couple sparse representation to filter the noisy pixels. During this second stage, conventional methods generally use only clean pixels to recover corrupted pixels, which can yield unsuccessful dictionary learning if the noise density is high and the number of useful clean pixels is inadequate. Therefore, we use reconstructed pixels to balance the deficiency. Experimental results show that the proposed noise detector has better performance than the conventional noise detectors. Also, with the information of noisy pixel location, the proposed impulse-noise removal method performs better than the conventional methods, through the recovered images resulting in better quality.

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References

  1. Rafael C. Conzalez, Richard E. Woods, "Image Restoration," Digital Image Processing, Prentice Hall, pp. 220-276. 2002.
  2. S.-J. Ko and Y. H. Lee, "Center weighted median filters and their applications to image enhancement," IEEE Trans. Circuits Syst., vol. 38, no. 9, pp. 984-993, Sep. 1991. https://doi.org/10.1109/31.83870
  3. Qin, Chuan, et al. "An inpainting-assisted reversible steganographic scheme using a histogram shifting mechanism," IEEE Transactions on Circuits and Systems for Video Technology 23.7, 1109-1118, 2013. https://doi.org/10.1109/TCSVT.2012.2224052
  4. Zhang, Xianquan, et al. "Salt and pepper noise removal with image inpainting," AEU-International Journal of Electronics and Communications 69.1, 307-313, 2015. https://doi.org/10.1016/j.aeue.2014.09.018
  5. Qin, Chuan, et al. "Visible watermark removal scheme based on reversible data hiding and image inpainting," Signal Processing: Image Communication, 60, 160-172, 2018. https://doi.org/10.1016/j.image.2017.10.003
  6. B. Xiong and Z. Yin, "A universal denoising framework with a new impulse detector and nonlocal means," IEEE Trans. Image Process., vol. 21, no. 4, pp. 1663-1675, Apr. 2012. https://doi.org/10.1109/TIP.2011.2172804
  7. L. Liu, C. L. P. Chen, Y. Zhou, and X. You, "A new weighted mean filter with a two-phase detector for removing impulse noise," Inf. Sci., vol. 315, pp. 1-16, Sep. 2015. https://doi.org/10.1016/j.ins.2015.03.067
  8. R. Garnett, T. Huegerich, C. Chui, and W. He, "A universal noise removal algorithm with an impulse detector," IEEE Trans. Image Process., vol. 14, no. 11, pp. 1747-1754, Nov. 2005. https://doi.org/10.1109/TIP.2005.857261
  9. Y. Dong, R. H. Chan, and S. Xu, "A detection statistic for random valued impulse noise," IEEE Trans. Image Process., vol. 16, no. 4, pp. 1112-1120, Apr. 2007. https://doi.org/10.1109/TIP.2006.891348
  10. Chen, Tao, and Hong Ren Wu. "Adaptive impulse detection using center-weighted median filters," IEEE Signal Processing Letters, vol. 8, no. 1, pp. 1-3, Jan. 2001. https://doi.org/10.1109/97.889633
  11. Abreu, Eduardo, et al. "A new efficient approach for the removal of impulse noise from highly corrupted images," IEEE transactions on image processing, 5.6, pp. 1012-1025, 1996. https://doi.org/10.1109/83.503916
  12. S. Schulte, M. Nachtegael, V.D. Witte, D. Van der Weken, E.E. Kerre, "A fuzzy impulse noise detection and reduction method," IEEE Trans. Image Process, 15, pp. 1153-1162, 2006. https://doi.org/10.1109/TIP.2005.864179
  13. S. Schulte, V. De Witte, M. Nachtegael, D. Van der Weken, E.E. Kerre, "Fuzzy random impulse noise reduction method," Fuzzy Sets Syst, 158, pp. 270- 283, 2007. https://doi.org/10.1016/j.fss.2006.10.010
  14. P.K. Sa, B. Majhi, "An improved adaptive impulsive noise suppression scheme for digital images," Int. J. Electron. Commun. (AEU), 64, 322-328, 2010. https://doi.org/10.1016/j.aeue.2009.01.005
  15. Ilke Turkmen, "The ANN based detector to remove random-valued impulse noise in images," J. Vis. Commun. Image R., vol. 34, pp. 28-36, Jan. 2016. https://doi.org/10.1016/j.jvcir.2015.10.011
  16. A. M. Bruckstein, D. L. Donoho, and M. Elad, "From sparse solutions of systems of equations to sparse modeling of signals and images," SIAM Rev., vol. 51, no. 1, pp. 34-81, 2009. https://doi.org/10.1137/060657704
  17. Shao, Ling, et al. "From heuristic optimization to dictionary learning: A review and comprehensive comparison of image denoising algorithms," IEEE Transactions on Cybernetics 44.7, pp. 1001-1013, 2014. https://doi.org/10.1109/TCYB.2013.2278548
  18. Dogra, Ayush, Bhawna Goyal, and Sunil Agrawal. "From multi-scale decomposition to non-multi-scale decomposition methods: A comprehensive survey of image fusion techniques and its applications," IEEE Access 5, 16040-16067, 2017. https://doi.org/10.1109/ACCESS.2017.2735865
  19. Jian Zhang, Debin Zhao, Wen Gao, "Group-Based Sparse Representation for Image Restoration," IEEE Trans. On Image Process., vol. 23, no. 8, pp. 3336-3351, Aug. 2014. https://doi.org/10.1109/TIP.2014.2323127
  20. Chen, Chun Lung Philip, et al. "Weighted couple sparse representation with classified regularization for impulse noise removal," IEEE Transactions on Image Processing, 24.11, pp. 4014-4026, 2015. https://doi.org/10.1109/TIP.2015.2456432
  21. K. Simonyan and A. Zisserman, "Very deep convolutional networks for large-scale image recognition," in Proc. of Computer Vision and Pattern Recongnition., 2015, pp. 1-14.
  22. Chen, Liang-Chieh, et al. "Deeplab: Semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected crfs," in Proc. Computer Vision and Pattern Recognition, 2017.
  23. Shin, Hoo-Chang, et al. "Deep convolutional neural networks for computer-aided detection: CNN architectures, dataset characteristics and transfer learning," IEEE transactions on medical imaging 35.5, pp. 1285-1298, 2016. https://doi.org/10.1109/TMI.2016.2528162
  24. He, Kaiming, et al. "Deep residual learning for image recognition," in Proc. of Proceedings of the IEEE conference on computer vision and pattern recognition. 2016.
  25. Zhang, Kai, et al. "Beyond a gaussian denoiser: Residual learning of deep cnn for image denoising," IEEE Transactions on Image Processing, 2017.
  26. M. Everingham, L. V. Gool, C. K. I. Williams, J. Winn, and A. Zisserman, "The PASCAL visual object classes (VOC) challenge," International journal of computer vision, 88.2 303-338, 2010. https://doi.org/10.1007/s11263-009-0275-4