Denoising Method Based On Wavelet Image Threshold

In recent years, digital image processing technology has developed rapidly and hasa broad application field. Image will inevitably produce noise in the process ofacquisition (digitized)、transmission and so on, so that the subsequent imageprocessing tasks are affected, how to extract the useful information from the noisysignal becomes a topic of a wide range of research.This is called the image denoising.There are many traditional denoising method, mostly using linear or averaging methodin spatial domain, but the effect is not very ideal, for low-pass denoising makes thedetails fuzzy and loss the useful high frequency information. How to reduce the noiseeffectively and keep edge information better becomes a major problem in denoisingfield.This article learns wavelet transform threshold denoising and discusses several keyproblems in the process of denoising in detail in view of the traditional denoisingmethods, such as denoising process, how to select the threshold, the principle and thecharacteristic of soft and hard threshold denoising method, then we compares of theirgood and bad points, conventional soft and hard threshold function in image denoisingin the presence of defects are analyzed on the basis of wavelet shrinkage thresholdproposed by Donoho, a new threshold method in denoising is used aiming at thedisadvantages of the traditional soft and hard threshold denoising, to improve softthresholding denoising principle by adding a coefficient formula in front of thethreshold, adjusting the value of m, n to obtain the new threshold function, the improvedfunction not only has good continuity, but also has a small distortion, has obtained thegood effect.The simulation experiment was done with Matlab7.0, take the adding white Gaussnoise assumption as a promise, the same images were used traditional denoising, softand hard threshold denoising and improved threshold denoising, and comparing theresults, analysis of data, experiments show that the improved scheme increases thesignal to noise ratio and peak signal to noise ratio compared with other methods, obtainsbetter denoising effect, not only to retain the traditional soft and hard threshold functionde-noising superiority, but also overcome the soft and hard threshold functionde-noising defects, retain the image details better at the mean time of denoising, clarityis also obviously improved, which proves that the improved program has effectiveness, feasibility and superiority.