Research Of Image Denoising Method Based On Contourlet Transform

As we enter the information age,the forms of information is no longer only speech,itevolves to include data, text, images, video and other multimedia forms. According tostatistics, seventy percenet of the information people accept outside comes from pictures.The technology of image processing is becoming more and more important. However,the images obtained by people are always polluted, which recuries higher demands forimage de-noising.In recent years,wavelet theory has been greatly developed and has been widely usedin image denoising. However,as it’s isotropic and its directional selectivity is poor, it canonly portray the singularity of an image midpoint, it can’t portray the high-dimensionalgeometry characteristics such as the texture and the edge. As a relatively new multi-scaletransformation, the contourlet transform can achieve relolution in any direction at anyscale,it has good performance at portray the contours and directional texture informationof the picture, it can make up the lack of the wavelet. This paper’s main work is asfollows:We will make a detailed introduction to the basic principles of contourlet transform,laying the foundation for the study of the subsequent de-noising algorithm.wewillintroducethe wavelet thresholding de-noising methods and the contourletthresholding de-noising methods,especially making a detailed description for their hardand soft thresholding and using matlab to do the experiments, compared to theirrespective values of PSNR and MSE, combined with the image effects, we do apreliminary analysis of their strengths and weaknesses.For the existing deficiencies, wepropose a shrinkage function based onmulti-parameter, And we will apply it to waveletand contourlet thresholdingde-noising method, valadating its efficiency andeffectiveness.Then we describethe hierarchical wavelet thresholding methods andcontourlet layered thresholding method, appliing the multi-parameter shrinkagefunctionto them, which further prove the validity of the multi-parametershrinkage function.