An Effective Shrinkage Threshold for Contourlet based on Image Denoising in Natural Images

Abstract: Image Denoising is an underlying problem in the field of image processing. In moreover, the intension of image denoising is to eliminate the noises as well as to conserve the details of an image as much as possible. Noise is a haphazard variation of image intensities and its shows as grains in the image. In this work Gaussian and Salt & Pepper noise are added to the input image. Contourlet is used for decomposition and the main advantage of Contourlet transform is to preserve the edges and contours. After decomposition, thresholding function are used such as Sure Shrink and Visu Shrink. These Shrinkage methods is to remove the noises effectively. After the noise removal, Performance of the denoised images is analysed and find the better threshold. Performance of natural image denoising is deliberate by Peak Signal to Noise Ratio (PSNR), Image Quality Index (IQI), Normalized Cross Correlation(NCC).

Keywords: Image Denoising, Contourlet, Shrinkages, PSNR.