为了更好地滤除图像中脉冲噪声和高斯噪声组成的混合噪声, 提出了一种基于非局部均值和Small Univalue Segment Assimilating Nucleus(SUSAN)算子的混合噪声滤除方法.该方法首先根据脉冲噪声点与角点之间吸收核同值区形状特征的不同, 采用SUSAN算子检测出大量的特征点, 特征点主要是脉冲噪声点, 也可能含有小部分角点.将特征点进行排序, 出现频次最高两位的点为脉冲噪声点.然后采用改进的均值滤波法计算脉冲噪声点邻域中非脉冲噪声点的均值, 以此替换脉冲噪声点灰度值.最后针对已滤除脉冲噪声的图像, 采用考虑了图像块信息的非局部均值方法滤除剩余的高斯噪声.去噪实验结果表明: 与自适应中值和加权均值结合的方法、中值滤波与小波结合的方法、脉冲耦合神经网络与中值滤波结合的方法相比, 本文方法主观视觉效果更好, 能够更好地保留图像中的边缘细节, 客观评价指标峰值信噪比有较大的提高, 滤除混合噪声的优势明显.

To remove mixed noise better, which is composed of impulse noise and Gaussian noise, a mixed noise removal method based on non-local means filter and Small Univalue Segment Assimilating Nucleus(SUSAN) operator was proposed. Firstly, according to the difference of shape features of univalue segment assimilating nucleus between the impulse noise points and conner points, the method extracts many characteristic points by SUSAN operator, which contains mainly impulse noise points and may also contain a small part of corner points. The characteristic points are sorted. The points whose frequency of occurrence ranks top two are impulse noise points. Then, the mean of non-impulse noise points in the pulse noise point′s neighborhood is calculated by the improved mean filter method to replace the impulse noise points′s gray value. Finally, for the image whose impulse noise has been filtered, the remaining Gaussian noise is denoised by non-local means filter method. A large number of denoising experimental results show that, compared with the method combining adaptive median with weighted mean, the method combining median filter with wavelet transform, and the method combining pulse coupled neural network with median filter, the proposed method has better subjective visual effect, and preserves the edges and details of image better, and it has great improvement in objective quantitative evaluation indicators such as peak signal to noise ratio. The proposed method shows obvious advantages in removing mixed noise.