The Algorithm Research of Image Denosing Based on Median Filter and Wavelet Transform
|School||Xi'an University of Science and Technology|
|Course||Applied Computer Technology|
|Keywords||Image Denoising Median filter Wavelet transform|
Image signal in the generation, transmission and recording process , often by a variety of noise interference , which seriously affect the visual effect of the image , so using appropriate methods to reduce noise ( image noise filtering ) , is a very important the work . In this paper, image de-noising method of the wavelet filtering technology and median filtering technology . First, a comprehensive overview of the status and common method of image denoising . Wavelet threshold filtering method for wavelet filtering technology has been widely used due to the simple calculation . Wavelet threshold filtering technology threshold function and threshold selection conducted in-depth research , this paper, an improved threshold function and threshold simulation test results show that this improved method has better denoising effect . Secondly, the study of the conventional median filter , the median filter is improved , minimax median filter , as well as better than the effect of the adaptive median filter algorithm , in Comparative analysis of their advantages and disadvantages in this paper a median filtering algorithm based on impulse noise detection and study its main properties . Simulation test shows this method has better denoising effect and detail preservation performance . Then on the image filtering mixed with a variety of noise filtering method . Median filter and wavelet filtering in the removal of the advantages of impulse noise and Gaussian noise , given an image mixed noise de-noising method . Median filtering based on impulse noise detection method based on wavelet characteristics of impulse noise and Gaussian noise , impulse noise detected and filtering , and Gaussian noise pollution pixels using wavelet threshold filtering method to remove Gaussian noise . The simulation test proved the practicality and effectiveness of this method has better effect than a single filter . Finally, at the end of this thesis work to do a detailed summary and image de-noising method further research directions are put forward .