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提出了一种基于D-S证据理论的小波萎缩图像去噪方法。对含噪图像进行离散平稳小波变换后,运用Bayes方法分得各层高频子带的小波萎缩系数,根据小波萎缩系数的空间及层间相关性,利用D-S证据理论的合成法则对初始小波萎缩系数进行融合修正。实验结果表明,该方法在有效地去除图像中的噪声的同时,还能较好地保留图像的边缘信息。算法在性能指标和视觉质量上均优于Donoho的小波软阈值去噪方法、传统的中值滤波法和Winner滤波法。
A wavelet shrinkage denoising method based on D-S evidence theory is proposed. After the discrete stationary wavelet transform of the noisy image, the wavelet atrophy coefficients of the high frequency subbands in each layer are obtained by the Bayes method. According to the spatial and inter-layer correlation of the wavelet shrinkage coefficients, the initial wavelet shrinkage Coefficients for fusion correction. The experimental results show that this method can effectively preserve the edge information of the image while effectively removing the noise in the image. The proposed algorithm outperforms Donoho ’s wavelet soft threshold denoising method, the traditional median filtering method and the Winner filtering method both in performance index and visual quality.