基于复数小波变换的图像滤噪

Image denosing based on complex wavelet transform

小波变换是一种在科学研究上有着广泛应用的工具。由于复数小波变换在某些方面比实数小波变换具有更多的优点,如:平移不变性、更好的方向性和精确的相空间信息等,可提高图像的去噪能力。本文采用二树复数小波变换,在基于H-Curve准则确定阈值的基础上进行图像去噪。此准则不需要提前知道噪声标准偏差,在实际应用中适用于不同类型的噪声,并且和目前多数方法去噪后的图像过于平滑相比,它还能产生较好的视觉效果。典型去噪试验表明:本文采用的方法在去噪能力、取得的视觉效果和确定阈值的广泛性方面都优于目前多数方法.

The wavelet transform is a tool widely used for many scientific purposes. According to more merits in some aspects over real wavelet transform such as shift invariability, more directions and precise information in phase space, complex wavelet transform can improve the ability to image denosing. In this paper, combined with H-Curve criterion, Dual-Tree complex wavelet transform is applied to image denosing. The H-Curve criterion does not need to know the noise standard deviation firstly,can dispose many kinds of noise practically and produces a better effect in vision over smooth image brought up by many methods presently. The typical denosing test shows that the method in this paper produced a better effect over multitudinous methods presently in many aspects such as the ability to denosing,the effect in vision and universality in choosing a threshold,etc.