基于MAP准则的红外图像小波域比例萎缩降噪和增强算法  

Denoising Algorithm of Proportional Shrinkage with Enhancement Based on the MAP Rule in Wavelet Domain for Infrared Image

作　　者：刘刚[1] 梁晓庚[1,2] 罗绪涛[1] 

机构地区：[1]西北工业大学自动化学院,西安710072 [2]洛阳光电技术发展中心,洛阳471009

出　　处：《计算机科学》2010年第4期274-277,共4页Computer Science

摘　　要：针对小波域比例萎缩降噪方法在去除噪声的同时也弱化了图像细节和边缘的缺陷,提出了具有增强效果的基于最大后验概率准则的小波域自适应降噪算法,并将之应用于红外图像降噪中。该算法在假定图像系数和噪声系数先验为高斯分布的基础上,利用最大后验概率准则计算小波系数的萎缩因子,然后在考虑尺度因素和方向能量因素的基础上对萎缩因子进行修正并将之应用于小波系数萎缩过程中,最后通过逆变换得到降噪和增强的图像。试验结果表明,在损失较小峰值信噪比值的情况下,提出的方法在增强图像细节和边缘、加大图像对比度等方面要优于直接比例萎缩,能够获得较好的视觉效果。给出的小波系数增强思想可以应用于基于其它降噪准则的比例萎缩算法中。In order to solve the fault of weakening the detail and edge of image while denoising in wavelet domain, this paper presented an adaptive denoising algorithm with detail enhancing and applied it to infrared image. On the basis of the assumption that the prior distribution of the original image coefficients and the noise coefficients were both Gaussian, this method firstly made use of the rule of Maximum a Posteriori to compute the shrinkable factor for wavelet coefficients, then revised it by taking decomposable level and directional energy into account. Finally, a denoising and enhancing image could be obtained when the wavelet coefficients which were shrunk by the revised shrinkable factor experienced the reverse transform. The experimental results show that the method given by this paper, compared with the direct proportional shrinkage, can enhance image＇s detail and improve image＇s contrast and get better visual effect though it has a little loss of Peak Signal Noise Ratio. The idea of coefficients＇ enhancement in wavelet domain proposed by this paper can apply to other proportional shrinkable algorithms.

关 键 词：小波域降噪 图像增强 比例萎缩 高斯分布 最大后验概率 峰值信噪比 

分 类 号：TP391.41[自动化与计算机技术—计算机应用技术]