Particle Swarm Optimization-Based SONAR Image Enhancement for Underwater Target Detection
| Autor: | Ganesan Kavitha, Dhilsha Rajapan, P. M. Rajeshwari, C.M. Sujatha |
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| Rok vydání: | 2014 |
| Předmět: |
Mean squared error
Computer science business.industry Ant colony optimization algorithms Computer Science::Neural and Evolutionary Computation Wiener filter MathematicsofComputing_NUMERICALANALYSIS ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION Particle swarm optimization Pattern recognition Sonar symbols.namesake Computer Science::Computer Vision and Pattern Recognition Median filter symbols Entropy (information theory) Artificial intelligence Underwater business |
| Zdroj: | Advances in Intelligent Systems and Computing ISBN: 9788132221258 |
| DOI: | 10.1007/978-81-322-2126-5_57 |
| Popis: | In the proposed work, particle swarm optimization (PSO) is employed to enhance SONAR images in spatial domain. A transformation function is used with the local and global data of the image to enhance the image based on PSO. The objective function considers the entropy of the image, number of edges detected, and the sum of edge intensities. PSO determines the optimal parameters required for image enhancement. PSO-based image enhancement is compared with median filter and adaptive Wiener filter both qualitatively and quantitatively. Mean square error (MSE) and peak signal-to-noise ratio (PSNR) are used as quantitative measures for the analysis of the enhanced images. MSE and PSNR analysis reveals that original details of the image are retained after enhancement. Based on the visual and quantitative analysis, it is considered that PSO-based technique provides better enhancement of SONAR images. |
| Databáze: | OpenAIRE |
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