An Image Impulsive Noise Denoising Method Based on Salp Swarm Algorithm

Full Text (PDF, 332KB), PP.43-51

Views: 0 Downloads: 0

Author(s)

Wei Liu 1 Ran Wang 1 Jun Su 1

1. Hubei University of Technology,China

* Corresponding author.

DOI: https://doi.org/10.5815/ijeme.2020.01.05

Received: 1 Dec. 2019 / Revised: 20 Dec. 2019 / Accepted: 15 Jan. 2020 / Published: 8 Feb. 2020

Index Terms

Image enhancement, Salp Swarm Algorithm, Median filtering, Noise elimination

Abstract

Image noise denoising is a very important task in image processing. Aiming at the shortcomings of traditional median filtering to handle image impulse noise, an approach based on Salp Swarm Algorithm (SSA) to eliminate image impulse noise is presented in the paper. In this method, the improved extremum method is used to detect the position of impulse noise pixels, and then the Salp Swarm algorithm is used to find the optimal pixel value instead of the noise pixel to complete the denoising process of the image. Experimental results testfies that image impulse noise could be effectively filtered out through the proposed method and the manipulated image is clear and more detail could be revealed for human vision. 

Cite This Paper

Wei Liu, Ran Wang, Jun Su, " An Image Impulsive Noise Denoising Method Based on Salp Swarm Algorithm ", International Journal of Education and Management Engineering(IJEME), Vol.10, No.1, pp.43-51, 2020. DOI: 10.5815/ijeme.2020.01.05

Reference

[1] Wang, Zhou , et al. "Image Quality Assessment: From Error Visibility to Structural Similarity." IEEE Transactions on Image Processing 13.4(2004).

[2] Tukey, Jw . Exploratory data analysis.. Exploratory data analysis.

[3] Lee, Chang Shing , S. M. Guo , and C. Y. Hsu . "Genetic-based fuzzy image filter and its application to image processing." IEEE Transactions on Systems Man & Cybernetics Part B Cybernetics A Publication of the IEEE Systems Man & Cybernetics Society 35.4(2005):694-711.

[4] Brownrigg, D. R. K. The weighted median filter. 1984.

[5] Hwang, H, and R. Haddad. "Adaptive median filters: new algorithms and results." Image Processing IEEE Transactions on 4.4(1995):499-502.

[6] Chen, Tao , K. K. Ma , and L. H. Chen . "Tri-state median filter for image denoising." IEEE Transactions on Image Processing A Publication of the IEEE Signal Processing Society8.12(1999):1834-1838.

[7] Eng, Howlung , et al. "Noise adaptive soft-switching median filter." IEEE Trans Image Process 10.2(2001):242-51.

[8] Zhang, Shuqun , and M. A. Karim . "A new impulse detector for switching median filters." IEEE Signal Processing Letters9.11(2002):360-363.

[9] Srinivasan, K. S. , and D. Ebenezer . "A New Fast and Efficient Decision-Based Algorithm for Removal of High-Density Impulse Noises." IEEE Signal Processing Letters 14.3 (2007):189-192.

[10] Xing Cang-ju, et al. "A New Filtering Algorithm Based on Extremum and Median Value" Journal of Image and Graphics 6.6(2001).(in Chinese)

[11] Lu, Ching Ta, and T. C. Chou. "Denoising of salt-and-pepper noise corrupted image using modified directional-weighted-median filter." Pattern Recognition Letters 33.10(2012):1287-1295.

[12] Faragallah, Osama S., and H. M. Ibrahem. "Adaptive switching weighted median filter framework for suppressing salt-and-pepper noise." AEU - International Journal of Electronics and Communications70.8(2016):1034-1040.

[13] LIU S X,WANG X C,CHANG C W. "Otus Image Segmentation Method Based on Improved Particle Swarm Optimization." Computer Science 40.8(2013):293-295.(in Chinese)

[14] WANG H T, LI D. "Research on image enhancement based on Improved Particle Swarm Optimization." Journal of Image and Graphics 34.6(2013):87-92.(in Chinese)

[15] Mirjalili, S., Gandomi, A. H., Mirjalili, S. Z., Saremi, S., Faris, H., & Mirjalili, S. M. "Salp swarm algorithm: A bio-inspired optimizer for engineering design problems." Advances in Engineering Software(2017). Windyga, P. S. "Fast impulsive noise removal. " IEEE Trans Image Process 10.1(2001):173-179.