IMAGE DENOISING USING WAVELET BASED CONTOURLET TRANSFORM

Abstract

The Contourlet transform consists of two modules: the Laplacian Pyramid and the Directional Filter Bank. When both of them use perfect reconstruction filters, the contourlet expansion and reconstruction is a perfect dual. Therefore, the contourlet transform can be employed as a coding scheme. The contourlet coefficients derived above can be transmitted through the wireless channel in the same way as transmitting the original image, where the transmission is prone to noise and block loss. However, the reconstruction at the receiver performs differently if the image is transmitted directly or coded by the contourlet transform. This paper studies the performance of the contourlet coding in image recovery and denoising. The simulation results show that for general images the contourlet transform is quiet competitive to the wavelet transform in the SNR sense and in the visual effect. Further, the contourlet transform can be used in a wireless face recognition system to extract the unique feature that other transforms cannot discover, For face recognition system, the recovery of the original image is not essential anymore; therefore, the resources on the image reconstruction from the contourlet coefficient can be saved.