Image denoising algorithm using mixed statistical model in complex wavelet packet transform
复小波包变换域混合统计模型图像降噪算法

The noisy image is decomposed into low frequency approximate subimages and high frequency directional subimages by using the quad-tree complex wavelet packet transform(QCWPT) which has the advantages of shift-invariance, high directional resolution and fine discrimination of high frequency signals. The complex coefficients in low frequency approximate subimages are kept unchanged, while the high frequency directional subimages are categorized as major type and minor type according to their inter-scale correlation. Noises in both types are removed by using of the non-Gaussian bivariate model and the zero mean Gaussian distributing model, respectively. In comparing either the power signal-tonoise ratio(PSNR) index or the visual effects with other methods, the presented scheme outperforms the traditional dualtree complex wavelet transform, QCWPT and wavelet domain Gaussian scale mixtures. Experiments also show that the presented scheme achieves an excellent balance between the suppression of noises and the preservation of image details and edge.