基于小波包变换的非局部均值去噪方法

在非局部均值滤波的基础上，为了更有效地去除图像噪声，提出一种基于小波包变换的非局部均值去噪算法。首先对图像进行小波包变换，通过小波域系数估计图像的高斯噪声参数，然后计算经小波包分解后高频子带内小波系数的相似度，并以此作为权值来对小波系数进行调整，最后通过小波包逆变换对图像进行重建。实验结果表明与传统的非局部均值去噪算法相比较，该算法能在去噪的同时有效地保持图像的边缘细节等信息，取得更好的去噪效果。

Non-local Mean Algorithm Based on Wavelet Packet Transform

Based on the non-local means （NL-means） filter algorithm, in order to improve the image quality, a NL-means algo-rithm based on wavelet packet transform is proposed. Firstly, the image is transformed by the wavelet packet, and the wavelet domain coefficients are applied to estimate the Gaussian noise parameters of the image, then the similarity of the high-frequency subband＇ s wavelet coefficients is calculated as the weights to adjust the wavelet coefficients, finally the image is reconstructed by the inverse wavelet packet transform. Experiment results show that this algorithm can preserve the edge detail information effectively, and get a superior denoising performance than the original non-local mean algorithm.