Image denoising using Contourlet and two-dimensional Principle Component Analysis

Abstract

This paper proposes a novel image denoising algorithm using the Contourlet transform and the two-dimensional Principle Component Analysis (2DPCA). The noise image can be decomposed by the Contourlet into directional subbands. The 2DPCA is then carried out to estimate the threshold for the image blocks in high frequency subbands. The soft thresholding shrinkage can hence be employed on the Contourlet coefficients without estimating the noise variance. The denoising algorithm is validated by numerical experiments on two images. Numerical results show that the proposed method can obtain higher PSNR than former methods.