基于Curvelet变换与小波包变换联合的图像去噪算法

小波包变换在处理图像中的平滑区域时能够起到较好的效果,而Curvelet变换可以更好地逼近线性奇异高维函数,对图像的边缘区域有最稀疏的表示.在此基础上提出了基于二者联合的图像去噪算法,在对含噪图像进行分割后,分别对线性区域和平滑区域采用Curvelet阈值去噪处理和小波包阈值去噪处理.该方法充分发挥了二者各自的优势,实验表明,它对图像的去噪效果要优于单纯的Curvelet或小波包去噪方法.

Image Denoising Based on Combining Curvelet Transform with Wavelet Packet Transform

Wavelet packet transform has good effect in smoothness of image while curvelet transform is a kind of optimal representation of image edges in the sense of nonlinear approximation. This paper proposes a new method, which is based on the combination of wavelet packet transform with curvelet transform, curvelet threshold denosing is used in edges while wavelet packet threshold denosing used in smoothness. This method gives full play to the respective advantages of the above two ways. Experiment shows that it has better denoising performance for image than the curvelet or wavelet alone.