新小波阈值函数在医学图像去噪中的应用

摘要

为了提高小波阈值去噪算法中的软阈值和硬阈值以及已有改进阈值函数存在的不足，提出了新的分层阈值函数的方法。该算法首先对噪声图像进行分解，从而得出小波系数。然后用改进的阈值函数对高频部分系数进行分层阈值处理。最后根据所得估计的小波系数在小波基的条件下，对图像进行重构，得到去噪后图像。该阈值函数具有优良的数学特性，通过对医学图像仿真实验结果表明，该算法去噪的效果无论是在视觉效果上，还是在均方差和信噪比性能分析上均优于常用的阈值函数，所以该算法在解决实际去噪问题中值得推广与应用。%In order to improve some shortcoming in the soft threshold and hard threshold and low threshold functions have improved of wavelet threshold denoising, the authors propose a new hierarchical denoising threshold. Firstly, the method decomposes the noise image. Thus it is concluded that wavelet coefficients. Secondly, the wavelet coefficient by using an improved threshold function was carried out on the high-frequency part of the threshold processing. Finally, according to the estimation in the wavelet base conditions, and to rebuild image, this method gets the denoised image. The method has excellent mathematical characteristics, through the medical image simulation results indicate that the de-noising method effect both in the visual effects. Or in the MSE and SNR performance analysis is better than above mentioned denoising methods. So the algorithm in solving the problem of actual denoising is worthy of popularization and application.