A Hybrid Bioinspired Approach for Advanced Image Reconstruction
With the exponential growth of digital imagery, some novel techniques for visual information reconstruction are needed since the development of high-speed and precision methods is still an open problem for several applications such as medical diagnosis, satellite imaging, and general image processin...
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| Format: | Article |
| Language: | English |
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IEEE
2024-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/10749834/ |
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| author | Salvador Lobato-Larios Oleg Starostenko Vicente Alarcon-Aquino |
| author_facet | Salvador Lobato-Larios Oleg Starostenko Vicente Alarcon-Aquino |
| author_sort | Salvador Lobato-Larios |
| collection | DOAJ |
| description | With the exponential growth of digital imagery, some novel techniques for visual information reconstruction are needed since the development of high-speed and precision methods is still an open problem for several applications such as medical diagnosis, satellite imaging, and general image processing. This paper proposes a new hybrid approach for reconstructing images using Particle Swarm Optimization (PSO), Genetic Algorithm (GA), and Wavelet Fusion (WF) techniques. While PSO seeks a solution modeled on the flocking and schooling patterns in birds and fish, GA helps to explore the solution space, by reducing the risk of local minima as well as improving the process of searching by modeling specific natural mechanisms at play in evolution, and WF enhances image quality by lessening noise. This is considered as the major contribution of the work, which lies in the bio-inspired algorithm by integrating swarm intelligence and wavelet fusion techniques applied to multiple initial reconstruction steps for an image to approximate the intended reconstructed one. Experimental results show that this hybrid approach converges fast and gives better reconstruction with a low Mean Squared Error (MSE). The proposed methodology provides a strong foundation for developing image reconstruction techniques by demonstrating that swarm intelligence can be integrated with wavelet-based techniques. |
| format | Article |
| id | doaj-art-34d0b1b462494ea784780490d231c1a3 |
| institution | Kabale University |
| issn | 2169-3536 |
| language | English |
| publishDate | 2024-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-34d0b1b462494ea784780490d231c1a32024-11-19T00:01:57ZengIEEEIEEE Access2169-35362024-01-011216594816596210.1109/ACCESS.2024.349555910749834A Hybrid Bioinspired Approach for Advanced Image ReconstructionSalvador Lobato-Larios0https://orcid.org/0009-0005-3765-1506Oleg Starostenko1https://orcid.org/0000-0002-9763-7651Vicente Alarcon-Aquino2https://orcid.org/0000-0002-9843-9219Institute of Environmental Studies, University of Sierra Juarez, Ixtlán de Juárez, Oaxaca, MexicoDepartment of Computing, Electronics and Mechatronics, Universidad de las Américas Puebla, San Andrés Cholula, MexicoInstitute of Environmental Studies, University of Sierra Juarez, Ixtlán de Juárez, Oaxaca, MexicoWith the exponential growth of digital imagery, some novel techniques for visual information reconstruction are needed since the development of high-speed and precision methods is still an open problem for several applications such as medical diagnosis, satellite imaging, and general image processing. This paper proposes a new hybrid approach for reconstructing images using Particle Swarm Optimization (PSO), Genetic Algorithm (GA), and Wavelet Fusion (WF) techniques. While PSO seeks a solution modeled on the flocking and schooling patterns in birds and fish, GA helps to explore the solution space, by reducing the risk of local minima as well as improving the process of searching by modeling specific natural mechanisms at play in evolution, and WF enhances image quality by lessening noise. This is considered as the major contribution of the work, which lies in the bio-inspired algorithm by integrating swarm intelligence and wavelet fusion techniques applied to multiple initial reconstruction steps for an image to approximate the intended reconstructed one. Experimental results show that this hybrid approach converges fast and gives better reconstruction with a low Mean Squared Error (MSE). The proposed methodology provides a strong foundation for developing image reconstruction techniques by demonstrating that swarm intelligence can be integrated with wavelet-based techniques.https://ieeexplore.ieee.org/document/10749834/Image reconstructionbioinspired algorithmsparticle swarm optimizationwavelet fusion |
| spellingShingle | Salvador Lobato-Larios Oleg Starostenko Vicente Alarcon-Aquino A Hybrid Bioinspired Approach for Advanced Image Reconstruction IEEE Access Image reconstruction bioinspired algorithms particle swarm optimization wavelet fusion |
| title | A Hybrid Bioinspired Approach for Advanced Image Reconstruction |
| title_full | A Hybrid Bioinspired Approach for Advanced Image Reconstruction |
| title_fullStr | A Hybrid Bioinspired Approach for Advanced Image Reconstruction |
| title_full_unstemmed | A Hybrid Bioinspired Approach for Advanced Image Reconstruction |
| title_short | A Hybrid Bioinspired Approach for Advanced Image Reconstruction |
| title_sort | hybrid bioinspired approach for advanced image reconstruction |
| topic | Image reconstruction bioinspired algorithms particle swarm optimization wavelet fusion |
| url | https://ieeexplore.ieee.org/document/10749834/ |
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