双密度复小波与系数相关性的红外图像去噪

Infrared image denoising based on dual density complex wavelet and coefficient correlation

为了去除红外图像中高斯噪声的同时,更好地保持和恢复图像的纹理边缘和细节特征,提出了基于双密度复小波与系数相关性的红外图像去噪方法。该方法充分利用双密度双树复小波在图像处理上的优势:图像信息的平移不变性、图像纹理细节的多方向选择性等,基于对图像小波系数分布的假设,根据当前小波系数与其父、子小波系数的相关性,对无噪的小波系数作贝叶斯估计,以恢复无噪的红外图像,最后对去噪图像进行引导滤波,去除图像的波纹效果。实验数据显示,该方法在EPI和FSIM以及图像的视觉效果上优于部分现有算法,证明该方法在噪声去除、纹理边缘的保持和恢复上具有更好的性能。

In order to remove the Gaussian noise in the infrared image while preserving and restoring the textures,edges and detailed features of the image,an infrared image denoising method based on dual density complex wavelet and coefficient correlation is proposed.This method makes the most of the advantages of dual density dual tree complex wavelet in image processing:translation invariance of image information,multi-directional selectivity of image texture and detail,etc.Based on the assumption of the distribution of image wavelet coefficients,according to the correlation between the current wavelet coefficient and its parent and child wavelet coefficients,Bayesian estimation is made for the noiseless wavelet coefficients to restore the noiseless infrared image.Finally,the denoised image is subjected to guided filter,so as to remove the ripple effect.Experimental data show that this method is superior to some existing algorithms in EPI,FSIM and visual effect of image,which proves that this method has better performance in noise removal,texture edge preservation and restoration.