基于层内和层间相关性的小波图像去噪

小波图像去噪已经成为图像去噪的主要方法之一.利用小波变换在去除噪声时,可提取并保存对视觉起主要作用的边缘信息,传统的小波去噪方法大致有小波阈值收缩去噪算法、小波模极大值去噪算法.由于小波系数间存在很大的相关性,本文提出了一种基于层内和层间相关性的小波去噪方法,利用图像细节信息在不同尺度及同一尺度上的相关性进行滤波,达到对低信噪比的图像去噪的目的.在实验中,将本文去噪的结果与Donoho的硬阈值作了比较,结果显示本文方法能获得较好的去噪效果.

Wavelet image denoising based on inter-scale and intra-scale dependency

Wavelet image denoising has been well acknowledged as an important method of image denoising.Although it can preserveedge information,present methods ignore relativity of wavelet coefficients.Traditional wavelet denoising methods generally have wavelet shrinkage denoising algorithm and wavelet modulus maximum algorithm.Considering dependency existing among wavelet coefficients,this paper proposes a wavelet denoising method based on inter-scale and intra-scale dependency,using image's detail information on all kinds of scale and the same scale.Experimental results show this algorithm can receive better denoising results.