Research On The Improved Algorithm Of BayesShrink Threshold Denoising

With the rapid development of modern image signal transmission,the processing of image information has become more and more important in modern society,and the clarity of the image and the acquisition of the detailed information have become more and more harsh.In the whole process of image processing will inevitably have a variety of interference signal generation,so the interference noise caused by image denoising research significance.In this paper,image denoising as the main content,first introduced the wavelet transform,multi-resolution analysis,hybrid Fourier-wavelet denoising,threshold noise reduction,image enhancement theory.At the same time,several denoising methods are described: spatial domain correlation denoising,wavelet threshold denoising,Bayesian threshold denoising.Then,the threshold denoising method is analyzed in detail,especially on the key issues such as the threshold quantity and the threshold function selection,and the theoretical analysis is carried out in the aspect enhancement of the image.Bayesian Atrophy Threshold Denoising Algorithm Because the implementation is relatively simple and the denoising effect is good,this paper proposes an improved threshold denoising algorithm based on traditional Bayesian denoising.And a large number of simulation experiments are carried out on the Matlab platform,and their corresponding denoising effect graphs are obtained.Compared with the traditional threshold function and threshold denoising algorithm,it can be proved that the improved denoising algorithm can eliminate the noise in the image more effectively.Finally,the algorithm is improved and then combined with the image edge enhancement method.The simulation results show that the proposed algorithm improves the peak signal to noise ratio and enhances the edge detail.