PRINCIP A L COMPONENT ANALYSIS BASED IMAGE DENOISIN G IMPLEMENTED USING LPG AND COMPAR ED TO WAVELET TRANSF ORM TECHNIQUES

Removal of noise is an important step in the image restoration process, but denoising of image remains a challenging problem in recent research associate with image processing. Denoising is used to remove the noise from corrupted image, while retaining the edges and other detailed features as much as possible. This noise gets introduced during acquisition, transmission & reception and storage & retrieval processes. We propose an efficient image denoising technique using wavelet based principal component ana lysis(PCA) with local pixel grouping(LPG).For a better preservation of image local structures, a pixel and its nearest neighbors are modelled as a vector variable, whose training samples are selected from the local window by using block matching based LPG. This method compares PSNR (Peak signal to noise ratio) between original image and noisy image and PSNR between original image and denoised image. The MSE and PSNR of the proposed method and local adaptive wavelet image denoising method are compared and de monstrated. Therefore, the image after denoising has a better visual effect.