基于父系数及邻域系数的DTCWT图像去噪方法

目前小波变换（DWT）在图像去噪中的应用取得了较好的效果，但DWT不具有位移不变性和良好的方向性。而二维双树复数小波变换（DTCWT）由于具有良好的平移不变性和方向选择性，比传统的二维离散小波变换具有更好的图像去噪能力。根据基于当前系数与父系数及邻域系数间的关系，本文构造了DTCWT图像去噪阈值计算公式，提出了一种去噪声新方法PNDTCWT（Parental and neighboring coefficients of DTCWT）。该方法在对图像进行二维DTCWT变换后，利用阈值公式根据当前系数和父系数及相邻系数计算收缩阈值，对当前系数进行去噪处理。最后经过二维DTCWT反变换，得到去噪结果。实验结果表明，PNDTCWT的噪声抑制效果明显优于各种基于DWT的去噪方法和其他DTCWT去噪方法。和基于父系数的DTCWT去噪方法相比，PNDTCW的PSNR平均提高了0.5dB左右。从视觉效果来看，PNDTCW在去除噪声的同时，能较好的保留图像细节，物体轮廓显得比较平滑，不存在传统DWT算法中的混淆现象。

A Novel Dual Tree Complex Wavelet Image De-noising Method Based on Parental and Neighboring Coefficients

By considering the advantages of the 2D Dual Tree Complex Wavelet Transfer(DTCWT) in shift invariance and directionality,a threshold denoising formula based on parental and neighboring coefficients is constituted and a novel Parental and Neighboring DTCWT(PNDTCWT) image denoising method is presented.By proposed method,the shrinkage threshold of each coefficient is calculated to use in denoising for the current coefficient.After 2D DTCWT transfer to an original image, the final image is obtained by the inver...