Adaptive Intelligent Single Particle Optimizer Based Image De-noising in Shearlet Domain

摘要

Adaptive intelligent single particle optimizer is proposed based on analyzing the deficiency of intelligent single particle optimizer, learning characteristics of particle swarm optimization, and introducing the Cauchy mutation. In the evolution of the algorithm, the particles not only learn from themselves, and can learn from their own historical experience, and finally can decide their velocity and position. Image edge blur is obtained by using the traditional nonlinear diffusion image de-noising method; Shearlet is a new-style multi-scale geometry analysis tool. It creates Shearlet functions, which have different characteristics through zooming, shearing translating and other affine transforming methods and enables its capable of optimally sparse representation. The paper proposed discusses adaptive intelligent single particle optimizer based image de-noising in Shearlet domain. Experimental results show that the method can effectively filter out image noise and better retain edge information, especially to the images containing abundant texture. Meanwhile, the de-noised images have higher Peak Signal to Noise Ratio.