提出了一种基于小波域分解的类型识别方法.该方法利用噪声图像的小波高频子带系数能量分布,对图像中最常出现的两类噪声:高斯噪声和椒盐噪声进行识别,并在此基础上对高斯噪声的方差和椒盐噪声的密度进行了估计.对大量含噪图像的实验结果表明:该方法对图像噪声类型的识别和噪声大小的估计都比较准确.

A novel methodology for identifying noise types and estimating noise level was reported. The energy distribution of the HH subband's coefficients in the wavelet domain was used to distinguish Gaussian white noise and salt & pepper noise, which exist in images frequently. And the variance of the Gaussian white noise and the density of salt & pepper noise were estimated. The experiments on a wide variety of images indicate the veracity of distinguishing and estimating of noise.