A Novel Wavelet Threshold Optimization via PSO for Image Denoising
Threshold selection is extremely important in wavelet transform for image denoising. The threshold selection problem can be viewed as continuous optimization problem. Recently, Particle Swarm Optimization was introduced to solve this problem, but its effectiveness is destroyed by the premature convergence. In order to overcome this drawback and obtain satisfactory effect, this paper proposes a modified chaos Particle Swarm Optimization algorithm for threshold selection, then adopts the optimal threshold achieved and a non-negative garrote function to process wavelet decomposed coefficients. When the premature convergence occurs, chaos search strategy will come into effect to help particles jump out of local optimization, and seek global optimization. Experimental results reveal the encouraging effectiveness of the proposed algorithm.
image thresholding denoising threshold selection Particle Swarm Optimization chaos search premature convergence
Xuejie Wang Yi Liu Yanjun Li
Key Laboratory of Intelligent System, Zhejiang University City College, Hangzhou, China School of Information & Electrical Engineering, Zhejiang University City College, Hangzhou, China
国际会议
杭州
英文
352-355
2011-10-28（万方平台首次上网日期，不代表论文的发表时间）