AN EFFICIENT SCHEME FOR DWT BASED PCA_PNLM ALGORITHM USING BIO-MEDICAL IMAGE DENOISING SYSTEM
Abstract
The main problem faced during biomedical image diagnosis is the noise introduced due to the consequence of the coherent nature of the image. The noise interfered may be Gaussian noise, speckle noise or Poisson noise, during transmission. The capturing devices itself has a salt & pepper noise. These noises corrupt the image and often lead to incorrect diagnosis. These noises make it more difficult for the observer to discriminate fine detail of the images in diagnostic examinations. Thus, denoising these noises from a noisy image has become the most important step in medical image processing. Here in this Paper a new algorithm DWT, Bilateral filter and PCA_ probable nonlocal means (PNLM) method for image denoising. Our main contributions are Point out defects of the weight function used in the classic NLM methods, Successfully derive all theoretical statistics of patch-wise differences for Gaussian noise, Employ this prior information and formulate the probabilistic weights truly reflecting the similarity between two noisy patches. Our simulation results indicate the PNLM outperforms the classic NLM and many NLM recent variants in terms of the peak signal noise ratio (PSNR) and the structural similarity (SSIM) index. Encouraging improvements are also found when we replace the NLM weights with the PNLM weights in tested NLM variants and Running Time Reduction Using Matlab 2014v.
Author
M.Meera banu,
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