Performance Evaluation and Comparative Analysis of Different Filters for Noise Reduction

Автор: Rupinder Kaur, Raman Maini

Журнал: International Journal of Image, Graphics and Signal Processing(IJIGSP) @ijigsp

Статья в выпуске: 7 vol.8, 2016 года.

Бесплатный доступ

The quality of microscopic images is generally degraded during the image acquisition by quantizing noise, electrical noise, light illumination etc. Noise reduction is considered as a very important preprocessing step as the quality of the images can determine the accuracy of the results. The work done focuses on the noise reduction using different filters on the different types of noises applied on the common digital images and specifically the Leukemia images. 40 images were taken for the comparison purpose; 20 digital images and 20 Leukemia images of different types of Leukemia. The qualitative as well as quantitative analysis of the performance of the filters on the different noises is done. For the quantitative analysis the parameters used for the evaluation of the images are MSE, PSNR and CoC. For the qualitative analysis visual analysis in terms of quality is also done using the resultant images and their histograms. Simulation has been done in Matlab 11b. From the test cases it has been observed that Adaptive Filter produces good results on Salt and Pepper, Speckle and Gaussian noise in case of the digital images. Whereas in case of Leukemia images results of Median Filter are best for the Gaussian, Poisson and Speckle noise corrupted images.

Еще

Peak Signal to Noise Ratio, Mean Square Error, CoC, Filters, Noise, Chronic Myelogenous Leukemia

Короткий адрес: https://sciup.org/15013992

IDR: 15013992

Список литературы Performance Evaluation and Comparative Analysis of Different Filters for Noise Reduction

  • L, Hailing, "Adaptive Gradient-Based and Anisotropic Diffusion Equation Filtering Algorithm for Microscopic Image Preprocessing", Journal of Signal and Information Processing, 4, pp. 82-87, 2013.
  • Pawan Patidar et. al., "Image De-noising by various filters for different noise", International Journal of Computer Applications, vol. 9, Issue 4, November 2010.
  • V. Rohit and A. Jahid, "A Comparative Study of Various Types of Image Noise and Efficient Noise Removal Techniques", International Journal of Advanced Research in Computer Science and Software Engineering, vol. 3, Issue 10, October 2013.
  • R. C. Gonzalez and R. E. Woods, "Digital Image Processing", Pearson Publications, 3rd edition, 2009.
  • M. V. Sarode and P. R. Deshmukh, "Reduction of Speckle Noise and Image Enhancement of Images Using Filtering Technique", International Journal of Advancements in Technology, vol 2, Issue 1, January 2011.
  • J. Chhikara and J. Singh, "Noise cancellation using adaptive algorithms", International Journal of Modern Engineering Research, Vol.2, Issue.3, pp-792-795, May-June 2012.
  • R. Lukac, B. Smolka, K. Martin, K. N. Plataniotis, and A. N. Venetsanopoulos, "Vector filtering for color imaging," IEEE Signal Processing Magazine, vol. 22, no. 1, pp. 74–86, 2005.
  • K. N. Plataniotis and A. N. Venetsanopoulos, "Color Image Processing and Application", Springer, New York, USA, 2000.
  • R. Lukac and K. N. Plataniotis, "A taxonomy of color image filtering and enhancement solutions," Advances in Imaging and Electron Physics, W. Hawkes, Ed., vol. 140, pp. 187–264, Elsevier, New York, USA, 2006.
  • S. Schulte, V. De Witte, and E. E. Kerre, "A fuzzy noise reduction method for color images," IEEE Transactions on Image Processing, vol. 16, no. 5, pp. 1425–1436, 2007.
  • P.-E. Ng and K.-K. Ma, "A switching median filter with boundary discriminative noise detection for extremely corrupted images," IEEE Transactions on Image Processing, vol. 15, no. 6, pp. 1506–1516, 2006.
  • Y. Li, F.-L. Chung, and S. Wang, "A robust neuro-fuzzy network approach to impulse noise filtering for color images," Applied Soft Computing Journal, vol. 8, no. 2, pp. 872–884, 2008.
  • S. Morillas, V. Gregori, and A. Hervás, "Fuzzy peer groups for reducing mixed Gaussian-impulse noise from color images," IEEE Transactions on Image Processing, vol. 18, no. 7, pp. 1452–1466, 2009.
  • T. Howlader and Y. P. Chaubey, "Noise reduction of DNA microarray images using complex wavelets,"IEEE Transactions on Image Processing, vol. 19, no. 8, pp. 1953–1967, 2010.
  • D. Zhai, M. Hao, and J. M. Mendel, "A non-singleton interval type-2 fuzzy logic system for universal image noise removal using quantum-behaved particle swarm optimization," in Proceedings of the IEEE International Conference on Fuzzy Systems (FUZZ '11), pp. 957–964, Taipei, Taiwan, June 2011.
  • T. Mélange, M. Nachtegael, and E. E. Kerre, "Fuzzy random impulse noise removal from color image sequences," IEEE Transactions on Image Processing, vol. 20, no. 4, pp. 959–970, 2011.
  • C. Brito-Loeza and K. Chen, "On high-order denoising models and fast algorithms for vector-valued images," IEEE Transactions on Image Processing, vol. 19, no. 6, pp. 1518–1527, 2010.
  • M. E. Yuksel and A. Basturk, "Application of type-2 fuzzy logic filtering to reduce noise in color images," IEEE Computational Intelligence MagazIne, vol. 7, no. 3, pp. 25–35, 2012.
  • Z. Xu, H. R. Wu, B. Qiu, and X. Yu, "Geometric features-based filtering for suppression of impulse noise in color images," IEEE Transactions on Image Processing, vol. 18, no. 8, pp. 1742–1759, 2009.
  • Y. Dong and S. Xu, "A new directional weighted median filter for removal of random-valued impulse noise," IEEE Signal Processing Letters, vol. 14, no. 3, pp. 193–196, 2007.
  • S. Schulte, S. Morillas, V. Gregori, and E. E. Kerre, "A new fuzzy color correlated impulse noise reduction method," IEEE Transactions on Image Processing, vol. 16, no. 10, pp. 2565–2575, 2007.
  • S. Schulte, V. De Witte, M. Nachtegael, D. vander Weken, and E. E. Kerre, "Fuzzy two-step filter for impulse noise reduction from color images," IEEE Transactions on Image Processing, vol. 15, no. 11, pp. 3567–3578, 2006.
  • Y. Li, G. R. Arce, and J. Bacca, "Weighted median filters for multichannel signals," IEEE Transactions on Signal Processing, vol. 54, no. 11, pp. 4271–4281, 2006.
  • J. Benesty, J. Chen and Y. Huang, "On the Importance of the Pearson Correlation Coefficient in noise reduction", IEEE Transactions on Audio, Speech and Language Processing, vol. 16, No. 4, May 2008.
  • F. Bergholm, J. Adler and I. Parmryd, "Analysis of Bias in the Apparent Correlation Coefficient Between Image Pairs Corrupted by Severe Noise", Journal of Mathematics Imaging and Vision, vol. 37, issue 3, pp. 204-219, July 2010.
  • Salomon, David, "Data Compression: The Complete Reference (4 ed.)", Springer p. 281, ISBN 978-1846286025, 2007.
  • P. Strobach, "Low-Rank Adaptive Filters", IEEE Transactions on Signal Processing, vol. 44, no. 22, Dec 1996.
  • J. W. Lee and G.K. Lee, "Design of an Adaptive Filter with a Dynamic Structure for ECG Signal Processing", International Journal of Control, Automation and Systems, vol. 3, no. 1, pp. 137-142, March 2005.
Еще
Статья научная