Contourlet image de-noising based on principal component analysis

Abstract

This paper proposes a new method which utilizes noise energy, instead of noise variance, to perform image de-noising based on Principal Component Analysis in Contourlet domain. The Contourlet transform is a new extension of the wavelet transform that provides a multi-resolution and multidirection analysis for two dimension images. Most of the existing methods for image de-noising rely on accurate estimation of noise variance. However, the estimation of noise variance is difficult in Contourlet domain. The novelty of this method is that it does not rely on the estimation of noise variance, therefore it has great value in solving real-world problems. We compared this method with the wavelet hard-thresholding and soft-thresholding methods which are commonly used in image de-noising. The experimental results show that the proposed approach can obtain better visual results and higher PSNR values, especially for the images that include mostly fine textures and contours. © Springer-Verlag Berlin Heidelberg 2007.