Image Denoising Based on Wavelet Modulus Maxima and Neyman-Pearson Principle Threshold
基于小波模极大值和Neyman-Pearson准则阈值的图像去噪

Firstly,this paper gives the property of wavelet transform of two dimensional noise,analyzes the relationship of wavelet transform modulus maxima to different decomposed class j and Lipschitz exponent,and points out how to determine and protect image edges.Then it explains the orthogonal wavelet transform of denoising based on soft and hard threshold,and puts forward a denoising method based on the wavelet modulus maxima and Neyman-Pearson principle. The method finds the optimal trade off between image denoising and protecting image edges.Based on the assumption that the observed image is the sum of the expected image and irregular corruptive noise,the qualitative and quantitative performance of our image denoising method is compared with others.Simulation results show the proposed method can efficiently denoise,such as increasing Signal-to-Noise Ratio(SNR),lowing Mean Square Error(MSE) and Relative Entropy(RE), while preserving the details of the original image.