Enhanced Genetic Algorithm Applied for Image Denoising Problem
DOI:
https://doi.org/10.37591/joci.v11i3.4436Abstract
This research introduces a new approach to restoring an image disrupted by salt and pepper (SPN) noise using a genetic algorithm (GA) at all densities, called Enhanced GA (EGA). The key contribution of the proposed algorithm is to merge the genetic algorithm with imaging approaches that are embedded into the population in order to achieve rapid convergence. The concept is to turn a group of entities into a variety of iterations using crossover and mutation operators. This method evolves a series of images rather than a series of filter parameters. Experimental simulation results on various images using a peak signal-to-noise ratio (PSNR), structural similarity index parameter (SSIM), demonstrate that the suggested algorithm outperforms other methods for eliminating SPN where the noise density is moderate and high.
Keywords: Enhanced genetic algorithm, Genetic algorithm, Noise removal, Salt and pepper.
References
S. B. S. Fareed and S. S. Khader, “Fast adaptive and selective mean filter for the removal of high-density salt and pepper
noise,” IET Image Process., vol. 12, no. 8, pp. 1378–1387, 2018.
N. Alaoui, A. B. H. Adamou-Mitiche, and L. Mitiche, “Effective hybrid genetic algorithm for removing salt and pepper
noise,” IET Image Process., vol. 14, no. 2, pp. 289-296(7), 2020.
Han, J., Yue, J., Zhang, Y., & Bai, L. (2015). Local sparse structure denoising for low-light-level image. IEEE
Transactions on Image Processing. https://doi.org/10.1109/TIP.2015.2447735
Lin, C. H., Tsai, J. S., & Chiu, C. Te. (2010). Switching bilateral filter with a texture/noise detector for universal noise
removal. ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings.
https://doi.org/10.1109/ICASSP.2010.5495475
Z. Yahya, “Modified tropical algebra based median filter for removing salt and pepper noise in digital image,” IET
Image Process., vol. 13, no. 14, pp. 2790-2795(5), 2019.
A. H. Lone and A. N. Siddiqui, “Noise models in digital image processing,” Glob. Sci-Tech, vol. 10, no. 2, p. 63, 2018.
J. Chen, Y. Zhan, H. Cao, and X. Wu, “Adaptive probability filter for removing salt and pepper noises,” IET Image
Process., vol. 12, no. 6, pp. 863–871, 2018.
G. Xiong, “Iterative grouping median filter for removal of fixed value impulse noise,” IET Image Process., vol. 13, no. 6,
pp. 946-953(7), 2019.
Tukey, J. W. (1977). Exploratory Data Analysis. https://doi.org/10.1007/978-1-4419-7976-6
Hwang, H., & Haddad, R. A. (1995). Adaptive Median Filters: New algorithms and results. IEEE Transactions on Image
Processing. https://doi.org/10.1109/83.370679
Wang, Z., & Zhang, D. (1999). Progressive switching median filter for the removal of impulse noise from highly
corrupted images. IEEE Transactions on Circuits and Systems II: Analog and Digital Signal Processing.
https://doi.org/10.1109/82.749102
Pattnaik, A., Agarwal, S., & Chand, S. (2012). A new and efficient method for removal of high density salt and pepper
noise through cascade decision based filtering algorithm. Procedia Technology.
https://doi.org/10.1016/j.protcy.2012.10.014
Esakkirajan, S., Veerakumar, T., Subramanyam, A. N., & PremChand, C. H. (2011). Removal of high density salt and
pepper noise through modified decision based unsymmetric trimmed median filter. IEEE Signal Processing Letters.
https://doi.org/10.1109/LSP.2011.2122333
Toh, K. K. V., & Isa, N. A. M. (2010). Noise adaptive fuzzy switching median filter for salt-and-pepper noise reduction.
IEEE Signal Processing Letters. https://doi.org/10.1109/LSP.2009.2038769
D. Zosso and A. Bustin, “A primal-dual projected gradient algorithm for efficient Beltrami regularization,” Comput. Vis.
Image Underst., pp. 14–52, 2014.
N. Asuni and A. Giachetti, “TESTIMAGES: a Large-scale Archive for Testing Visual Devices and Basic Image
Processing Algorithms,” in Smart Tools and Apps for Graphics - Eurographics Italian Chapter Conference, 2014.
Z. Wang, A. C. Bovik, H. R. Sheikh, and E. P. Simoncelli, “Image quality assessment: from error visibility to structural
similarity,” IEEE Trans. Image Process., vol. 13, no. 4, pp. 600–612, 2004.
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