Research Of Image Denoising Method Based On Contourlet Transform

In the process of transmission and digitization,images are often affected by the interference of imaging equipment and external environment noise,which reduces the resolution of digital images and deviates from the authenticity of the image itself.This not only affects people's subjective visual effects,but also seriously hinders the subsequent processing of images.Therefore,image denoising is the basic and crucial step in digital image processing.The research of image denoising technology has important theoretical and commercial value.The main research work of this paper is to realize the better image denoising effect in different noise environment by using the non sampling Contourlet transform.First of all,the basic theory of wavelet transform is the basis of the study,and its shortcomings in image processing applications are analyzed.Due to the shortage of the basic theory of wavelet transform,Contourlet transform is introduced and a variety of filter,analyzes the advantages,also points out the defects of translation invariant and mixing phenomenon,so as to deeply discuss the theory of the Nonsubsampled Contourlet transform,to solve the pseudo Gibbs phenomenon Contourlet.Secondly,in the case of Gauss white noise,using Nonsubsampled Contourlet transform,for high frequency coefficients part,the processing using adaptive threshold function threshold and improve the energy structure of the correlation coefficient between the scale,the realization of image noise reduction.Furthermore,according to the actual situation,in the case of Gauss white noise and mixed salt and pepper noise,we propose a median filter to reduce the salt and pepper noise in the low frequency part,so as to improve the contrast of the image.Through experiments,the simulation of MATLAB software isused to compare the noise reduction effect of the proposed method and the noise reduction effect of wavelet transform using Bayes Shrink threshold hard threshold function and soft threshold function.Through subjective and objective evaluation of the two aspects,we get the work done in this paper to improve the noise reduction effect of the image.