中文说明：图像去噪的统计优化的自适应小波包 （WP） 阈值函数基于广义高斯分布。它适用于噪声的图像，以获得最佳的树或最优小波基，利用信息熵计算高效多级 WP 分解。它选择自适应的阈值，即水平和子带依赖分析子带系数的统计参数的基础。在利用的阈值函数中，基于最大后验概率估计，每个系数和相应的子带的均值之间的最优线性内插法估计占主导地位的系数修改后的版本。实验结果表明，在不同噪声强度条件下的几个测试图像表明提出的算法，称为公开进修学院-收缩，产生更好的峰值信噪比和卓越的视觉图像质量 — — 通用图像质量指标用来衡量 — — 相对于标准的去噪方法，特别是在高噪声强度。它也优于一些最佳状态的现行基于小波变换的去噪技术。
English Description：
The statistical optimized adaptive wavelet packet (WP) thresholding function for image denoising is based on generalized Gaussian distribution. It is suitable for noisy images to obtain the best tree or wavelet basis, and uses information entropy to calculate efficient multilevel WP decomposition. It selects adaptive thresholds, that is, the level and subband dependence analysis are based on the statistical parameters of subband coefficients. In the used threshold function, based on the maximum a posteriori probability estimation, the optimal linear interpolation between each coefficient and the mean value of the corresponding subband estimates the modified version of the dominant coefficient. Experimental results show that several test images under different noise intensities show that the proposed algorithm, called Open Learning Institute shrinkage, produces better peak signal-to-noise ratio and superior visual image quality - a common image quality indicator used to measure - relative to standard denoising methods, especially at high noise intensities. It is also superior to some of the best state denoising techniques based on wavelet transform.