A Novel Blind Image Source Separation Using Hybrid Firefly Particle Swarm Optimization Algorithm

Authors

  • A. Khalfa LASS Laboratory, Department of Electronics, University Mohamed Boudiaf of M’Sila, Algeria
  • M. Sahed LASS Laboratory, Department of Electronics, University Mohamed Boudiaf of M’Sila, Algeria
  • E. Kenane LGE Laboratory, Department of Electronics, University Mohamed Boudiaf of M’Sila, Algeria
  • N. Amardjia LIS Laboratory, Department of Electronics, Ferhat Abbas Setif University 1, Algeria https://orcid.org/0000-0002-7177-4260
Volume: 12 | Issue: 6 | Pages: 9680-9686 | December 2022 | https://doi.org/10.48084/etasr.5255

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, SSIM

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How to Cite

[1]
A. Khalfa, M. Sahed, E. Kenane, and N. Amardjia, “A Novel Blind Image Source Separation Using Hybrid Firefly Particle Swarm Optimization Algorithm”, Eng. Technol. Appl. Sci. Res., vol. 12, no. 6, pp. 9680–9686, Dec. 2022.

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