Wavelet Denoising Research For Moter Image

The paper study on wavelet denoising for motorcar images before motor's registrations are recognized.First, I have set up mathematical models of motorcar images blured by raindrop and spurted slurry and appended two kinds of noises to motorcar images.Secondly, I have studied how to evaluate images'quality, especially, how to evaluate a method of images'quality with image residuals and histograms.Thirdly, I have selected of best wavelet functions. Added values of PSNR between noising image and denoising image , as well as saved energy after denoising is applied ,their computing results indicate: (1) Effect that a motorcar image appended normal zero mean gauss noise with level 25 is better by following wavelet functions: sym4～sym,db8～db15,coif2～coif5,bior2.8,bior4.4,bior6.8 ;(2) Effect that a motorcar image appended peper & salt noises with density 0.2 is better by following wavelet functions: sym8,db15,coif5,bior6.8.Fourthly, I have applied a suitable threshold function. After a image appended normal zero mean gauss noises from level 1 to level 60 is denoised by some typical wavelet functions with soft and hard threshold function, two soft and hard threshold curves are drawn ,whose PSNR is changed with noise level. The charts of curves indicate that two curves have one crossing (critical value), when noise levels are smaller than"critical value"; hard threshold function is better, or, soft threshold function is better. after a motorcar image appended peper & salt noises from density 0.01 to 0.6 is denoised by some typical wavelet functions with soft and hard threshold function, two soft and hard threshold curves are made ,whose PSNR change with noise density. The charts of curves indicate that two curves have one crossing (critical value) too, when noise density are smaller than"critical value", results are as same as the image appended gauss noises.Fifthly, How to set up threshold. by bior6.8 wavelet ,a motorcar image include of noise is decomposited three levels. According to image residuals and histograms, threshold selecting on every details of one level and on every levels is researched. results indicate: there are poor useful informations in every details of first level and in diagonal...