An Effective Denoising Method for Images Contaminated with Mixed Noise Based on Adaptive Median Filtering and Wavelet Threshold Denoising

  • Lin, Lin (Dept. of Information Engineering, Weihai Vocational College)
  • Received : 2017.02.13
  • Accepted : 2017.06.10
  • Published : 2018.04.30


Images are unavoidably contaminated with different types of noise during the processes of image acquisition and transmission. The main forms of noise are impulse noise (is also called salt and pepper noise) and Gaussian noise. In this paper, an effective method of removing mixed noise from images is proposed. In general, different types of denoising methods are designed for different types of noise; for example, the median filter displays good performance in removing impulse noise, and the wavelet denoising algorithm displays good performance in removing Gaussian noise. However, images are affected by more than one type of noise in many cases. To reduce both impulse noise and Gaussian noise, this paper proposes a denoising method that combines adaptive median filtering (AMF) based on impulse noise detection with the wavelet threshold denoising method based on a Gaussian mixture model (GMM). The simulation results show that the proposed method achieves much better denoising performance than the median filter or the wavelet denoising method for images contaminated with mixed noise.


  1. Y. H. Lee and S. B. Rhee, "Wavelet-based image denoising with optimal filter," Journal of Information Processing Systems, vol. 1, no. 1, pp. 32-35, 2005.
  2. J. Y. Lu, H. Lin, D. Ye, and Y. S. Zhang, "A new wavelet threshold function and denoising application," Mathematical Problems in Engineering, vol. 2016, article no. 3195492, pp. 1-8, 2016.
  3. S. Tania and R. Rowaida, "A comparative study of various image filtering techniques for removing various noisy pixels in aerial image," International Journal of Signal Processing, Image Processing and Pattern Recognition, vol. 9, no. 3, pp. 113-124, 2016.
  4. S. M. M. Rahman and M. K. Hasan, "Wavelet-domain iterative center weighted median filter for image denoising," Signal Processing, vol. 83, no. 5, pp. 1001-1012, 2003.
  5. J. Li, W. Zhao, and J. Li, "Image denoising for mixed noise," Journal of Lanzhou Jiaotong University, vol. 26, no. 4, pp. 116-118, 2007.
  6. Y. Ma and J. Li, "A novel method based on adaptive median filtering and wavelet transform in noise images," in Proceedings of the IEEE 3rd International Conference on Communication Software and Networks, Xi'an, China, 2011, pp. 626-629.
  7. H. Dong and F. Wang, "Image-denoising based on bior wavelet transform and median filter," in Proceedings of the Symposium on Photonics and Optoelectronics, Shanghai, China, 2012, article no. 6270998, pp. 1-3.
  8. P. S. J. Sree, P. Kumar, R. Siddavatam, and R. Verma, "Salt-and-pepper noise removal by adaptive median-based lifting filter using second-generation wavelets," Signal, Image and Video Processing, vol. 7, no. 1, pp. 111-118, 2013.
  9. J. Wu, "Wavelet domain denoising method based on multistage median filtering," Journal of China Universities of Posts and Telecommunications, vol. 20, no. 2, pp. 113-119, 2013.
  10. A. Joshi, A. K. Boyat, and B. K. Joshi, "Impact of wavelet transform and median filtering on removal of salt and pepper noise in digital images," in Proceedings of the International Conference on Issues and Challenges in Intelligent Computing Techniques, Ghaziabad, India, 2014, pp. 838-843.
  11. S. K. Agarwal and P. Kumar, "Denoising of a mixed noise color image through special filter," International Journal of Signal Processing, Image Processing and Pattern Recognition, vol. 9, no. 1, pp. 159-176, 2016.
  12. X. Zhang and S. Zhang, "Diffusion scheme using mean filter and wavelet coefficient magnitude for image denoising," AEU - International Journal of Electronics and Communications, vol. 70, no. 7, pp. 944-952, 2016.
  13. C. Zhang and K. Wang, "A switching median-mean filter for removal of high-density impulse noise from digital images," Optik - International Journal for Light and Electron Optics, vol. 126, no. 9-10, pp. 956-961, 2015.
  14. O. S. Faragallah and H. M. Ibrahem, "Adaptive switching weighted median filter framework for suppressing salt-and-pepper noise," AEU - International Journal of Electronics and Communications, vol. 70, no. 8, pp. 1034-1040, 2016.
  15. J. W. Tukey, Exploratory Data Analysis. Reading, MA: Addison-Wesley, 1977.
  16. M. Juneja and R. Mohan, "An improved adaptive median filtering method for impulse noise detection," International Journal of Recent Trends in Engineering and Technology, vol. 1, no. 1, pp. 274-278, 2009.
  17. D. L. Donoho and I. M. Johnstone, "Ideal spatial adaptation by wavelet shrinkage," Biometrika, vol. 81, no. 3, pp. 425-455, 1994.
  18. S. G. Chang, B. Yu, and M. Vetterli, "Adaptive wavelet thresholding for image denoising and compression," IEEE Transactions on Image Processing, vol. 9, no. 9, pp. 1532-1546, 2000.