ACO Based Adaptive Filter for High Density Impulse Noise

Authors(2) :-Rahul Malhi, P. S. Maan

In this paper, we proposed an ACO based adaptive non-casual linear prediction technique for vector median filter to remove high density impulse noise from color images. It is a new method for impulsive noise reduction and edge preservation in images. Generally, when an image is affected by high density of impulse noise, homogeneity among the pixels is distorted. Images of different characteristics corrupted with a wide range of impulsive noise densities using impulsive noise model is examined using the proposed method. This paper, based on the basic ant colony algorithm and integrating with the genetic algorithm, proposes an image restoration processing method based on hybrid ant colony algorithm. This method transforms the optimal population information of genetic algorithm into the original pheromone concentration matrix of ant colony algorithm and uses it to compute the parameters of degradation function to get a precise estimate of the original image. By analyzing and comparing the restoration results, the method of this paper cannot only overcome the influence of noises, but it can also make the image smoother with no fringe effects in the edges and excellent visual effects, verifying its practicability. The proposed filter improves the Peak Signal to Noise Ratio (PSNR).

Authors and Affiliations

Rahul Malhi
Research Scholar, Computer Science & Engineering, DAV Institute of Engineering & Technology, Jalandhar, Punjab, India
P. S. Maan
Assistant Professor, Computer Science & Engineering, DAV Institute of Engineering & Technology, Jalandhar, Punjab, India

Image Restoration, Impulse Noise, ACO (Ant Colony Optimization), Adaptive filtering

  1. K. Sakthidasan Sankaran , N. Velmurugan Nagappan, Noise free image restoration using hybrid filter with adaptive genetic algorithm, Computers and Electrical Engineering 000 (2016) 1–11.
  2. Zhang H, Yang J, Zhang Y, Huang T. Image and video restoration via non-local kernel regression. IEEE Trans Cybern 2013; 43(3):1035–46
  3. Lien C-Y, Huang C-C, Chen P-Y, Lin Y-F, An efficient denoising architecture for removal of impulse noise in images. IEEE Trans Computer 2013; 62(4):631–43.
  4. Zhao JF, Feng H-J, An improved image restoration approach using adaptive local constraint. Opt Int J Light Electron Opt 2012; 123(11):982–5
  5. R H Chan, H W Chung, M Nikolova, Salt-and-Pepper noise removal by median-type noise detectors and detail-preserving regularization, IEEE Transaction on Image Processing. 14(10) (2005) 1479-1485.
  6. R H Chan, C Hu, M Nikolova, An iterative procedure for removing random –valued impulse noise, IEEE Signal Processing Letters. 11(12) (2004) 921-924.
  7. H Yu, L Zhao, H Wang, An efficient procedure for removing random valued impulse noise in images, IEEE Signal Processing Letters. 15 (2008) 922-925.
  8.  J Astola, P Haavisto, Y Neuvo, Vector median filters, Procedings of IEEE. 78(4) (April 1990) 678-689.
  9. T Sun, Y Neuvo, Detail-preserving median based filters in image processing, Pattern Recognition Letters.15 (1994) 341-347.
  10. S J Ko, Y H Lee, Centre weight median filters and their applications to image enhancement, IEEE Transaction on Circuits and System. 38(9) (1999) 984-993.
  11. Z Wang, D Zhang, Progressive switching median filter for the removal of impulse noise from highly corrupted images, IEEE Transaction on Circuits System II. 46(1) (1999) 78–80.
  12. S Zhang and M A Karim, A new impulse detector for switching median filters, IEEE Signal Process Letter. 9(4) (2002) 360–363.
  13. C Jhang, K Wang, Removal of high-density impulse noise based on switching morphology-mean filter, International Journal of Electronics and Communications (AEÜ). 69 (2015) 226-135.
  14. T Chen, K Ma, L H Chen, Tri-State median filter for image denoising, IEEE Transaction on Image Processing. 8(12) (December 1999) 1834-1838.
  15. L Alparone, S Baronti, R Carla, Two-dimensional rank-conditioned median filter, IEEE Transaction.IEEE Express Letters on Circuits and Systems-II, Analog and Digital Signal Processing. 42( 2) (February 1995) 130-132.
  16. S Morillas, V Gregori, G Peris-Fajarnes, P Latorre, A Fast impulse color image filter using fuzzy metrices, Journal of Real-Time Image Processing. 11(2005) 417-428.
  17. H L Eng, K K Ma, Noise adaptive soft-switching median filter, IEEE Transaction on Image Processing. 10(2) (2001) 242-251.
  18. K M Singh, P K Bora, Adaptive vector median filter for removal impulses from color images, Circuits and Systems, ISCAS’03. Proceedings of the 2003 International Symposium. 2 (2003) 396-399.
  19. S K Meher, B Singhawat, An improved recursive and adaptive median filter for high density impulse noise, International Journal of Electronics and Communications (AEÜ). 68 (2014) 1173-1179.
  20. Amarjit Roy, Rabul Hussain Laskar (2017) “Non-casual linear prediction based adaptive filter for removal of high density impulse noise from color images” International Journal of Electronics and Communications (Elsevier) S1434-8411(16)31416-9.
  21. S Schulte, V D Witte, et al., Histogram based fuzzy color filter for image restoration, Image andVision Computing 25 (2007) 1377–1390.
  22. S Masood, A Hussain, et al., Color difference based Fuzzy filter for extremely corrupted color Images, Applied Soft Computing 21 (2014) 107-118.
  23. J Wu, C Tang, Random-valued impulse noise removal using fuzzy weighted non-local means, Signal, image, and video processing 8 (2014) 349-355.

Publication Details

Published in : Volume 2 | Issue 5 | September-October 2017
Date of Publication : 2017-10-31
License:  This work is licensed under a Creative Commons Attribution 4.0 International License.
Page(s) : 536-540
Manuscript Number : CSEIT172599
Publisher : Technoscience Academy

ISSN : 2456-3307

Cite This Article :

Rahul Malhi, P. S. Maan, "ACO Based Adaptive Filter for High Density Impulse Noise", International Journal of Scientific Research in Computer Science, Engineering and Information Technology (IJSRCSEIT), ISSN : 2456-3307, Volume 2, Issue 5, pp.536-540, September-October-2017.
Journal URL :

Article Preview