Optimum Median Filter Based on Crow Optimization Algorithm

Main Article Content

Basma Jumaa Saleh
Ahmed Yousif Falih Saedi
Ali Talib Qasim al-Aqbi
Lamees abdalhasan Salman


          A novel median filter based on crow optimization algorithms (OMF) is suggested to reduce the random salt and pepper noise and improve the quality of the RGB-colored and gray images. The fundamental idea of the approach is that first, the crow optimization algorithm detects noise pixels, and that replacing them with an optimum median value depending on a criterion of maximization fitness function. Finally, the standard measure peak signal-to-noise ratio (PSNR), Structural Similarity, absolute square error and mean square error have been used to test the performance of suggested filters (original and improved median filter) used to removed noise from images. It achieves the simulation based on MATLAB R2019b and the results present that the improved median filter with crow optimization algorithm is more effective than the original median filter algorithm and some recently methods; they show that the suggested process is robust to reduce the error problem and remove noise because of a candidate of the median filter; the results will show by the minimized mean square error to equal or less than (1.38), absolute error to equal or less than (0.22) ,Structural Similarity (SSIM) to equal (0.9856) and getting PSNR more than (46 dB). Thus, the percentage of improvement in work is (25%).


Download data is not yet available.

Article Details

How to Cite
Saleh BJ, Saedi AYF, al-Aqbi ATQ, Salman L abdalhasan. Optimum Median Filter Based on Crow Optimization Algorithm. Baghdad Sci.J [Internet]. [cited 2021Mar.4];18(3):0614. Available from: https://bsj.uobaghdad.edu.iq/index.php/BSJ/article/view/4525


Gonzalez R C. Digital_Image_Processing. 2nd ed; 2002. 793 p.

Kang C, Wang W. Fuzzy reasoning-based directional median filter design. Signal Process. 2009; 89 (3): 344–351.

Zhu Y, Huang C. An Improved Median Filtering Algorithm for Image Noise Reduction. Phys. Procedia. 2012; 25: 609–616.

Wang Z, Zhang D. Progressive switching median filter for the removal of impulse noise from highly corrupted images. IEEE Trans. Circuits Syst. 1999; 46 (1): 78–80.

Hwang H, Haddad RA. Adaptive median filters: new algorithms and results. IEEE Trans Image Process. 1995; 4 (4): 499–502.

Vijaykumar VR, Ebenezer D. High Density Impulse Noise Removal Using Robust Estimation Based Filter. IAENG Int. J. Comput. Sci. 2008; 35 (3).

Lyakhov PA, Orazaev AR, Chervyakov NI, Kaplun DI. A New Method for Adaptive Median Filtering of Images. In2019 IEEE Conference of Russian Young Researchers in Electrical and Electronic Engineering (EIConRus) 2019 Jan 28 (pp. 1197-1201). IEEE.

Uğur E, Gökrem L, Engino S. Different applied median filter in salt and pepper noise. Comput. Electr. Eng. 2018; 70: 789-798.

Jin L, Zhu Z, Song E, Xu X. An effective vector filter for impulse noise reduction based on adaptive quaternion color distance mechanism. Signal Process. 2018; 147: 173–189.

Karthik B, Kumar TK, Vijayaragavan SP, Sriram M. Removal of high density salt and pepper noise in color image through modified cascaded filter.J. Ambient Intell Human Comput. 2020 Feb 1:1-8.

Saleh BJ, Al-Aqbi ATQ, Saedi AYF. A novel biogeography inspired trajectory-following con- troller for national instrument robot. CCIS. 2018; 938: 171–189.

Askarzadeh A. A novel metaheuristic method for solving constrained engineering optimization prob- lems: Crow search algorithm. Comput. Struct. 2016: 169: 1–12.

Ma Z, Wu RH, Feng D. Partition-based vector filtering technique for suppression of noise in digital color images. IEEE Trans. Image Process. 2006; 15 (8): 2324–2342.

Xu J, Wang L, Shi Z. A switching weighted vector median filter based on edge detection. Signal Process. 2014; 98: 359–369.

Guo D, Xiaobo Q, Xiaofeng D, Keshou W, Xuhui Ch. Salt and Pepper Noise Removal with Noise Detection and a Patch-Based Sparse Representation. Adv. Multimedia. 2014: 1-14.

Huang KW, Girsang AS, Wu ZX, Chuang YW. A hybrid crow search algorithm for solving permutation flow shop scheduling problems. Appl. Sci. 2019; 9 (7): 1–15.

MedPix Medical Image Database. https://library.tmc.edu/website/medpix-medical-image-database, 2019.

Gallagher N, Wise G. A Theoretical Analysis of the Properties of Median Filters. IEEE Trans. Acoust, Speech, Signal Process. 1981; 29: 1136-1141.

Pok G, Jyh-Charn L. Decision based median filter improved by predictions. ICIP. 1999; 2: 410-413.

Luo W. An efficient detail-preserving approach for removing impulse noise in images. IEEE Signal Process. Lett. 2006; 13 (7): 413–416.

Lukac R. Adaptive vector median filtering. Pattern Recogn. Lett. 2003; 24 (12):1889–1899.

Lukac R, Smolka B. Application of adaptive center-weighted vector median framework for the enhancement of cdna microarray images. Int J Appl Math Comput Sci. 2003; 13 (3):369–383.

Smolka B, Malik K, Malik D. Adaptive rank weighted switching filter for impulsive noise removal in color images. J Real-Time Image Proc. 2012; 10(2): 289–311.

Chanu, P, Singh KH. A two-stage switching vector median filter based on quaternion for removing impulse noise in color images. Multimed Tools Appl. 2019; 78: 15375–15401.

Wang G, Liu Y, Zhao T. A quaternion-based switching filter for colour image denoising. Signal Process. 2014; 102: 216–225.

Jin L, Zhu Z, Song E, Xu X. An effective vector filter for impulse noise reduction based on adaptive quaternion color distance mechanism. Signal Process. 2018; 147: 173–189.

Esakkirajan S, Veerakumar T, Subramanyam AN, Premchand CH. Removal of high density salt and pepper noise through modified decision based unsymmetric trimmed median filter. IEEE Signal Process. Lett. 2011; 18(5): 287- 290.

Rambabu TG, Kishore KK. Medical image denoising using KPCA with local pixel grouping. In2015 International Conference on Computer Communication and Informatics (ICCCI) 2015 Jan 8 (pp. 1-5). IEEE.

Chanu PR, Singh KM. A Switching vector median filter for impulse noise removal from color images. Proceedings of TENCON. 2017, 5-8th Nov., Penang, Malaysia: 2819–2824.

Jin L, Zhu Z, Xu X, Xiang L. Two-stage quaternion switching vector filter for color impulse noise removal. Sign. Process. 2016; 128:171–185.

Chanu PR, Singh KM. Impulse Noise Removal from Medical Images by Two Stage Quaternion Vector Median Filter. J. Med. Syst. 2018; 42: 197.

Yadav AK, Roy R, Kumar R, Kumar CS, Kumar AP. Algorithm for de-noising of color images based on median filter. In2015 third International Conference on Image Information Processing (ICIIP). 2015 Dec 21 (pp. 428-432). IEEE.