小波构造变正则参数变分模型在带噪图像恢复中的应用
Wavelet Based Variational Approach to Adaptive Regularization Parameter for Noisy Image Restoration
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摘要: 在利用正则化方法构造变分模型进行图像去噪时,其正则参数往往选择为恒定值.文中利用小波分解的层次性和带噪图像中噪声所具有的时频特点,构造出变正则参数的变分模型.在不同的小波分解层,通过选择不同的正则参数从而达到自适应去噪的目的.Abstract: In noise removal the regularization parameter is traditionally global. In the new approach a variational model \min _g\|f-g\|_L_2(R)^2+\alpha R(g) is constructed, where g is in some wavelets space. Through wavelets pyramidal decomposition and relying on the different time-frequency properties of noise and signal, a certain regularization parameter is chosen adaptively, and different parameters are chosen in different levels for adaptive noise removal.
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