摘要
为适应图像的空域非平稳变化,提出了一种基于小波域分类隐马尔可夫树(CHMT)模型的图像去噪方法。该模型中,图像在每一尺度每一子带的小波系数均被分成C组以突出其空域非平稳变化的特征,这样原来的一棵小波四叉树被分成了C棵具有不同HMT参数的小波四叉树,再经过合理的初始化和期望最大化(EM)算法训练参数,反变换恢复。实验结果表明,与已有方法相比,该方法在不增加计算量的前提下,明显改善了所恢复图像的质量(PSNR)。
In order to adapt spatial nonstationary character of an image, a denoising method based on Wavelet-Domain Classified Hidden Markov Tree Model (CHMT) is proposed.In this method,image's coefficients of every scale and subband are divided into C groups to emphasize the spatial nonstationary character,so that one image corresponds with C HMTs.Then these coefficients are initialized,trained by EM algorithm and inverse-transformed.Test result shows that this method improves image quality (PSNR) obviously while calculation doesn't add.
出处
《红外与激光工程》
EI
CSCD
北大核心
2005年第2期232-235,共4页
Infrared and Laser Engineering
关键词
CHMT模型
去噪
EM算法
初始化
Algorithms
Image quality
Markov processes
Mathematical models
Trees (mathematics)
Wavelet transforms
