基于改进伪中值滤波和非局部均值滤波的红外图像滤波方法

首先对伪中值滤波算法进行了改进:噪声检测过程融入像素点灰度值、几何距离等因素,实现噪声点从图像像素点中的逐步分离;采用加权滤波的方法滤除噪声。其次对改进非局部均值滤波算法的先验信息获取方法进行了改进:对噪声图像进行提升小波变换,采用一种新型阈值函数选择低频分解系数,对高于阈值的系数进行重构得到参考图像,计算参考图像的相似度权值并将其作为改进非局部均值滤波算法的先验信息。最后基于2种改进算法提出了一种红外图像滤波方法,即依次采用改进伪中值滤波算法和基于先验信息的改进非局部均值滤波算法对红外图像进行滤波处理,然后将其与参考图像进行融合,以修正被过度滤波的图像。实验结果表明,该方法针对高密度噪声的红外图像有较好的滤波效果。

A filtering method for infrared image based on improved pseudo median filtering and non-local means filtering

Firstly,pseudo median filtering algorithm was improved.Factors of gray value and geometric distance of pixels are integrated into noise detection process so as to separate noise from image pixels.The noise is filtered by weighted filtering method.Then obtaining method of prior information of an improved non-local means filtering algorithm was improved.The noise image is conducted lifting wavelet transform.The low-frequency coefficients are selected by a new threshold function,and the selected low-frequency coefficients which are greater than the threshold are reconstructed to obtain a reference image.Weighted value of the reference image is computed and regarded as prior information.Finally,a filtering method for infrared image based on the two improved algorithms was proposed,namely using the two improved algorithms to filter noise in infrared image successively,then correcting the filtered image by the reference image.The experimental results show that the method has good filter effect for infrared image with high density noises.