A Novel Blind Image Source Separation Using Hybrid Firefly Particle Swarm Optimization Algorithm
Received: 11 August 2022 | Revised: 3 September 2022, 22 September 2022, and 30 September2022 | Accepted: 2 October 2022 | Online: 9 October 2022
Corresponding author: M. Sahed
Abstract
Signal and image separation are extensively used in numerous imaging applications and communication systems. In this paper, a novel Blind Source Separation (BSS) approach, based on the Hybrid Firefly Particle Swarm Optimization (HFPSO), is proposed for separating mixed images. This approach processes the observed source without any prior knowledge about the model and the statistics of the source signal. The proposed method presents high robustness against local minima and converges quickly to the global minimum. Via numerical simulations, the proposed approach is tested and validated in comparison with standard Particle Swarm Optimization (PSO), Robust Independent Component Analysis (RobustICA), and Artificial Bee Colony (ABC) algorithms. The obtained results show that the presented technique outperforms the existing ones in terms of quality of image separation, the Signal-to-Noise Ratio (PSNR), and Structural Similarity Index Measure (SSIM). Moreover, the obtained results demonstrate that our approach provides also promising results in image separation from noisy mixtures.
Keywords:
Blind Image Separation, Hybrid Firefly Particle Swarm Optimization, PSNR, SSIMDownloads
References
J. Herault, C. Jutten, and B. Ans, "Detection de grandeurs primitives dans un message composite par une architeture de calcul neuromimetique en apprentissage non supervise," in 10° Colloque sur le traitement du signal et des images, Nice, France, Jan. 1985, pp. 1017–1022.
G. D. Pelegrina, L. T. Duarte, and C. Jutten, "Blind source separation and feature extraction in concurrent control charts pattern recognition: Novel analyses and a comparison of different methods," Computers & Industrial Engineering, vol. 92, pp. 105–114, Feb. 2016. DOI: https://doi.org/10.1016/j.cie.2015.12.017
H. Buchner, E. Petersen, M. Eger, and P. Rostalski, "Convolutive blind source separation on surface EMG signals for respiratory diagnostics and medical ventilation control," in 2016 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Orlando, FL, USA, Dec. 2016, pp. 3626–3629. DOI: https://doi.org/10.1109/EMBC.2016.7591513
F. J. Theis, "Uniqueness of complex and multidimensional independent component analysis," Signal Processing, vol. 84, no. 5, pp. 951–956, May 2004. DOI: https://doi.org/10.1016/j.sigpro.2004.01.008
S. Mavaddaty and A. Ebrahimzadeh, "Blind signals separation with genetic algorithm and particle swarm optimization based on mutual information," Radioelectronics and Communications Systems, vol. 54, no. 6, Jul. 2011, Art. no. 315. DOI: https://doi.org/10.3103/S0735272711060045
S. Mavaddaty and A. Ebrahimzadeh, "Evaluation of Performance of Genetic Algorithm for Speech Signals Separation," in 2009 International Conference on Advances in Computing, Control, and Telecommunication Technologies, Bangalore, India, Sep. 2009, pp. 681–683. DOI: https://doi.org/10.1109/ACT.2009.173
Y. Yang, X. Wang, and D. Zhang, "Blind Source Separation Research Based on the Feature Distance Using Evolutionary Algorithms," International Journal of Acoustics and Vibrations, vol. 19, no. 4, pp. 276–281, Dec. 2014. DOI: https://doi.org/10.20855/ijav.2014.19.4360
L. Chen, L. Y. Zhang, and Y. J. Guo, "Blind Image Separation Method Based on Artificial Bee Colony Algorithm," Advanced Materials Research, vol. 468–471, pp. 583–586, 2012. DOI: https://doi.org/10.4028/www.scientific.net/AMR.468-471.583
S. Mavaddati, "Blind Voice Separation Based on Empirical Mode Decomposition and Grey Wolf Optimizer Algorithm," Iranian Journal of Electrical and Electronic Engineering, vol. 15, no. 3, pp. 330–342, Sep. 2019.
J. Kennedy and R. Eberhart, "Particle swarm optimization," in Proceedings of ICNN’95 - International Conference on Neural Networks, Aug. 1995, vol. 4, pp. 1942–1948.
X.-S. Yang, "Firefly Algorithms for Multimodal Optimization," in Stochastic Algorithms: Foundations and Applications, Berlin, Heidelberg, 2009, pp. 169–178. DOI: https://doi.org/10.1007/978-3-642-04944-6_14
X.-S. Yang, "Firefly algorithm, stochastic test functions and design optimisation," International Journal of Bio-Inspired Computation, vol. 2, no. 2, pp. 78–84, Nov. 2010. DOI: https://doi.org/10.1504/IJBIC.2010.032124
N. M. Okasha, "Reliability-Based Design Optimization of Trusses with Linked-Discrete Design Variables using the Improved Firefly Algorithm," Engineering, Technology & Applied Science Research, vol. 6, no. 2, pp. 964–971, Apr. 2016. DOI: https://doi.org/10.48084/etasr.675
M. F. Masouleh, M. A. A. Kazemi, M. Alborzi, and A. T. Eshlaghy, "A Genetic-Firefly Hybrid Algorithm to Find the Best Data Location in a Data Cube," Engineering, Technology & Applied Science Research, vol. 6, no. 5, pp. 1187–1194, Oct. 2016. DOI: https://doi.org/10.48084/etasr.702
A. Nickabadi, M. M. Ebadzadeh, and R. Safabakhsh, "A novel particle swarm optimization algorithm with adaptive inertia weight," Applied Soft Computing, vol. 11, no. 4, pp. 3658–3670, Jun. 2011. DOI: https://doi.org/10.1016/j.asoc.2011.01.037
M. Taherkhani and R. Safabakhsh, "A novel stability-based adaptive inertia weight for particle swarm optimization," Applied Soft Computing, vol. 38, pp. 281–295, Jan. 2016. DOI: https://doi.org/10.1016/j.asoc.2015.10.004
İ. B. Aydilek, "A hybrid firefly and particle swarm optimization algorithm for computationally expensive numerical problems," Applied Soft Computing, vol. 66, pp. 232–249, May 2018. DOI: https://doi.org/10.1016/j.asoc.2018.02.025
V. Zarzoso and P. Comon, "Robust Independent Component Analysis by Iterative Maximization of the Kurtosis Contrast With Algebraic Optimal Step Size," IEEE Transactions on Neural Networks, vol. 21, no. 2, pp. 248–261, Oct. 2010. DOI: https://doi.org/10.1109/TNN.2009.2035920
L. C. Chan and P. Whiteman, "Hardware-Constrained Hybrid Coding of Video Imagery," IEEE Transactions on Aerospace and Electronic Systems, vol. AES-19, no. 1, pp. 71–84, Jan. 1983. DOI: https://doi.org/10.1109/TAES.1983.309421
Z. Wang, A. C. Bovik, H. R. Sheikh, and E. P. Simoncelli, "Image quality assessment: from error visibility to structural similarity," IEEE Transactions on Image Processing, vol. 13, no. 4, pp. 600–612, Apr. 2004. DOI: https://doi.org/10.1109/TIP.2003.819861
N. Diffellah, Z. E. Baarir, F. Derraz, and A. Taleb-Ahmed, "A Global Variational Filter for Restoring Noised Images with Gamma Multiplicative Noise," Engineering, Technology & Applied Science Research, vol. 9, no. 3, pp. 4188–4195, Jun. 2019. DOI: https://doi.org/10.48084/etasr.2737
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