摘要
针对传统SVD图像去噪方法的不足,提出了一种基于SVD分解的小波分解图像去噪方法。通过对小波变换的系数矩阵进行奇异值分解,将其中的信号特征成分和噪声分解到不同的正交子空间中,在子空间中选取集成信号特征成分的奇异值矢量进行重构,从而提取出淹没在噪声中的信号成分。实验结果表明该文提出的方法适用于图像信号的提取,与传统的SVD去噪方法相比,它提取出的信号特征成分更完整,信噪比更高。
Aiming at the deficiency of traditional SVD image denoising method, a new continuous wavelet transform （CWT） image denoising method based on SVD was presented. Through the singular value decomposition of the matrix of CWT, the SVD was applied to decompose the signal features and noise into different orthogonal sub-spaces. With the reconstruction of the singular vectors in sub-space, the signal features were extraeted. The experiment results show that the approach is appropriate to extract images signal, and compared with the traditional methods, the signal feature components extracted by the approach are completer and with higher SNR.
出处
《数字通信》
2009年第3期87-89,共3页
Digital Communications and Networks
关键词
图像去噪
奇异值分解
小波变换
image denoising
singular value decomposition （SVD）
wavelet transform
