基于贝叶斯准则的小波域自适应消噪阈值

Adaptive wavelet denoising threshold for based on bayesian rule

为了从噪声图像中最大程度地恢复出原始清晰的图像,从图像小波分解数据的统计特性出发,将近指数模型作为分解层间小波系数的先验分布,用以描述各尺度小波分解系数的相关性。进而基于贝叶斯准则,提出了一种新型的自适应小波图像去噪阈值。实验结果表明,该类阈值体现了图像小波系数在各尺度间的相关性,其去噪性能要好于现有的阈值算法。

After analyzing an exponentially decaying inter-scale model of image wavelet coefficients, a subband-adaptive thresholding algorithm is proposed based on Bayesian rule. The new approach outperforms other methods because it captures the statistical inter-scale property of wavelet coefficients,and it is more adaptive to the data of each subband. Experiments show that higher peak-signal-to-noise ratio and better subjective visual effect can be obtained, which are better than those of other thresholding denoising algorithms.