Image denoising based on wavelet analysis and quantum-behaved particle swarm optimization
JunhuiZhou1, JieLiu2
COMPUTER MODELLING & NEW TECHNOLOGIES 2014 18(12B) 541-547
1School of Information, Hunan Vocational College for Nationalities, Yueyang 414000, Hunan, China
2School of Architecture, Harbin Institute of Technology, Harbin150006,Heilongjiang,China
This paper investigates the basic principle of threshold denoising based on wavelet transform, including the selection of wavelet basis, the determination of wavelet decomposition level, the selection method of threshold and the threshold estimation method of wavelet coefficient. Additionally, it proposes an image denoising method based on quantum-behaved particle swarm optimization (QPSO), gives the optimization value based on the experiments and theoretical analysis and optimizes the dynamic threshold by using numerous advantages of wavelet transform in the field of image denoising and QPSO so as to realize the self-adaptive denoisingof wavelet transform and reduce the influence of subjective factors. The simulation experiment shows that in addition to the effective denoising, the algorithm of this paper protects the image details and obtains better image denoising effects.