Application of Fast Particle Swarm Optimization Algorithm in Image Denoising

Abstract

The wavelet contraction law is the most widespread image denoising method at present, although the people used the wavelet transformation to carry on the image denoising to obtain certain progress, the effect was still not very ideal. Proposed one new optimized wavelet threshold value contraction law algorithm is based on the fast particle swarm. Particle swarm optimized algorithm was used to extract the threshold value optimal solution, then particle swarm optimal solution is used as the wavelet decomposition each criterion threshold value, carrying on the image denoising by this most superior threshold value solution. The experiment proved that the method, not only PSNR is obvious enhancement, but also the picture quality and vision are improved, moreover it is bigger along with the noise variance, the PSNR and image quality is better.KeywordsParticle Swarm OptimizationParticle SwarmParticle Swarm Optimization AlgorithmImage DenoisingWavelet ThresholdThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.