基于复小波与最大后验估计的红外图像滤波  

Infrared image filtering based on complex wavelet and maximum a posteriori estimation

作　　者：吴丽华 聂丰英[2] WU Lihua;NIE Fengying(School of Information Engineering,Jiangxi Vocational College of Mechanical and Electrical Technology,Nanchang 330013,China;School of Information and Artificial Intelligence,Nanchang Institute of Science and Technology,Nanchang 330108,China)

机构地区：[1]江西机电职业技术学院信息工程学院,江西南昌330013 [2]南昌工学院信息与人工智能学院,江西南昌330108

出　　处：《光学技术》2023年第1期113-119,共7页Optical Technique

基　　金：江西省高等学院教学改革研究课题(JXJG-17-70-2);江西省教育厅科学技术研究项目(GJJ212517);江西省教育科学“十三五”规划课题(19YB266)。

摘　　要：针对现有的红外图像去噪算法在边缘恢复和保持上的缺陷,提出了基于双树复小波与最大后验估计的红外图像去噪方法。充分利用双树复小波变换的多分辨率分析、平移不变性和多方向选择性等优秀特性,对含噪的红外图像作双树复小波变换;基于对高斯噪声和无噪图像的概率密度分布的假设,在小波域中对无噪图像的小波系数作最大后验估计,实现红外图像的去噪和恢复。红外图像去噪实验证明了方法的有效性,算法在有效去除噪声的同时,对边缘细节的保持和恢复较理想,去噪的图像质量指标PSNR和SSIM比现有的方法分别提高1dB和2%以上。Aiming at the defects of existing infrared image denoising algorithms in edge restoration and preservation, an infrared image denoising method based on complex wavelet and maximum a posteriori estimation is proposed. This method takes full advantage of the excellent characteristics of dual tree complex wavelet transform, such as multi-resolution analysis, translation invariance and multi-directional selectivity, performs dual tree complex wavelet transformation for noisy infrared image;and based on the assumption of probability density distribution of Gaussian noise and noiseless image, the maximum a posteriori estimation of wavelet coefficients of noiseless image is made in the wavelet domain to realize the denoising and restoration of infrared images. Infrared image denoising experiments have proven the effectiveness of the proposed method, this method not only removes noise effectively, but also maintains and recovers the edge details perfectly, the image quality indexes PSNR and SSIM of denoising are 1dB more and 2% higher than the existing methods, respectively.

关 键 词：红外图像去噪 高斯噪声 阈值函数 双树复小波 最大后验估计 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]