Evaluation of Noise Exclusion of Medical Images Using Hybridization of Particle Swarm Optimization and Bivariate Shrinkage Methods

Shruti Bhargava, Ajay Somkuwar


Denoising of images got corrupted by addition of noise signals (generated by no single reason) has always a subject of interest for researchers. This paper proposes and classifies the efficiency of an algorithm based on bivariate shrinkage further optimized by Particle Swarm Optimization (PSO).The estimator for undecimatedfilterbank which incorporate the adaptive subbands thresholding further represented with singal threshold based on denosing performs.The paper evaluate performance of medical image denoising by calculation of PSNR, MSE, WPSNR and SSIM. The simulation results based on testing the model at MATLAB 2010A platform shows significant enhancement in mitigation of Gaussian noise, speckle noise, poisson noise and salt & pepper noises from experimental data


DWT (Discrete Wavelet Transform), MSE (Mean Square Error),PSNR (Peak Signal to Noise Ratio), Wavelet De-noising,

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DOI: http://doi.org/10.11591/ijece.v5i3.pp421-428

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International Journal of Electrical and Computer Engineering (IJECE)
p-ISSN 2088-8708, e-ISSN 2722-2578