基于双密度双树复小波变换的局域自适应图像去噪

提出一种基于双密度双树复小波变换的局域自适应图像去噪算法。首先，分析了双密度双树复小波变换的原理及特点，给出了双变量收缩函数(BSF)的推导。然后，对噪声图像并行使用四个二维双密度离散小波变换，且行和列采用不同的滤波器组，实现对噪声图像的双密度双树复小波分解。根据小波系数的统计特性以及层内和层间系数的相关性，采用结合局域方差估计的双变量收缩函数对小波系数进行处理。用收缩后的小波系数重构去噪图像。最后，将该算法用于灰度图像和彩色图像去噪。实验结果表明：在噪声方差为30时，经该算法去噪后图像与噪声图像相比，获得最高的峰值信噪比增益达11.72dB，平均结构相似度最高增加2.7倍，复合峰值信噪比增益达11.68dB。且对不同噪声方差下的不同噪声图像，该算法在滤除噪声的同时保留更多的细节，去噪图像的视觉质量得到很大的改善。

Local Adaptive Image Denoising based on Double-Density Dual-Tree Complex Wavelet

In order to improve the quality of the degraded images,an efficient local adaptive image denoising algorithm based on the Double-density Dual-tree Complex Wavelet Transform(DD-DT CWT) is proposed.The principles and characteristics of the DD-DT CWT are analyzed and a Bivariate Shrinkage Function(BSF) is derivated.Then,the noise image decomposition by the DD-DT CWT is implemented by applying four 2-D Double-density Discrete Wavelet Transform(DD DWT) in parallel and distinct filter sets in the rows and columns...