关键词: 小波域降噪，图像增强，比例姜缩，高斯分布，最大后验概率，峰值信噪比

Abstract: 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.

Key words: Denoising in wavelet domain, Image's enhancement,Proportional shrinkage, Gaussian distribution, Maximum a posteriori, Peak signal noise ratio