Denoising Using Contourlet Transform, ICA, and Chaotic Particle Swarm Optimization
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This study employs Contourlet transform, Independent Component Analysis (ICA), and chaotic particle swarm optimization for image denoising. These techniques collectively improve image quality by effectively reducing noise artifacts. The Contourlet transform serves as a multiscale decomposition method that captures directional and detailed image features through directional filter banks and pyramidal structures. ICA operates as a blind source separation technique that decomposes mixed signals into statistically independent components using optimization algorithms like FastICA or Infomax. The chaotic particle swarm optimization algorithm enhances parameter selection in denoising processes by introducing chaotic maps (e.g., logistic maps) to escape local optima, with implementation involving fitness evaluation and velocity updates. By integrating these methodologies, the approach yields clearer and more accurate image results with optimized noise separation capabilities.
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