Particle Swarm Optimization for Gray-Scale Image Noise Cancellation

2008 ◽  
Author(s):  
Te-Jen Su ◽  
Tzu-Hsiang Lin ◽  
Jia-Wei Liu
Keyword(s):  
Particle Swarm Optimization ◽  
Particle Swarm ◽  
Noise Cancellation ◽  
Image Noise ◽  
Gray Scale ◽  
Swarm Optimization ◽  
Gray Scale Image

Research and Design of Adaptive Noise Cancellation Based on Particle Swarm Optimization Algorithm

2012 ◽  
Vol 479-481 ◽  
pp. 1942-1945
Author(s):  
Jie Zhang ◽  
Shi Qi Jiang
Keyword(s):  
Particle Swarm Optimization ◽  
Particle Swarm ◽  
Pso Algorithm ◽  
Noise Cancellation ◽  
Biological Species ◽  
Lms Algorithm ◽  
Adaptive Noise Cancellation ◽  
Step Size ◽  
Swarm Optimization ◽  
Adaptive Noise

Particle swarm optimization (PSO) is a kind of evolutionary computation technology which simulates the behavior of biological species. The essence of adaptive noise cancellation (ANC) is adjust the weight value of filter based on the input signals, the LMS algorithm is commonly used in this system, However, the convergence behavior and maladjustment of the LMS algorithm is seriously affected by the step-size μ, and the optimum value of μ cannot be determined easily, In this paper, Particle Swarm Optimization with linear decreasing inertia weight is proposed to solve the filter problem instead of LMS, taking the FIR filter of ANC as example, the simulation shows that ANC based on the PSO algorithm is better than classic ANC based on the LMS algorithm, and it gives the satisfactory results.

Multilevel Image Segmentation Using Modified Particle Swarm Optimization

2017 ◽  
pp. 106-142 ◽  
Author(s):  
Sourav De ◽  
Firoj Haque
Keyword(s):  
Particle Swarm Optimization ◽  
Image Segmentation ◽  
Optimization Technique ◽  
Real Life ◽  
Particle Swarm ◽  
Pso Algorithm ◽  
Gray Scale ◽  
Swarm Optimization ◽  
Modified Particle Swarm Optimization ◽  
Segmented Images

Particle Swarm Optimization (PSO) is a well-known swarm optimization technique. PSO is very efficient to optimize the image segmentation problem. PSO algorithm have some drawbacks as the possible solutions may follow the global best solution at one stage. As a result, the probable solutions may bound within that locally optimized solutions. The proposed chapter tries to get over the drawback of the PSO algorithm and proposes a Modified Particle Swarm Optimization (MfPSO) algorithm to segment the multilevel images. The proposed method is compared with the original PSO algorithm and the renowned k-means algorithm. Comparison of the above mentioned existing methods with the proposed method are applied on three real life multilevel gray scale images. For this purpose, three standard objective functions are applied to evaluate the quality of the segmented images. The comparison shows that the proposed MfPSO algorithm is done better than the PSO algorithm and the k-means algorithm to segment the real life multilevel gray scale images.

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