基于HMT模型的图像去噪方法研究

Image denoising method based on hidden markov tree

小波图像去噪已经成为图像去噪的主要方法之一。利用小波变换在去除噪声时,可提取并保存对视觉起主要作用的边缘信息,但现有的去噪声方法忽略了小波系数之间的相关性。针对这一不足,在小波域隐Markov树模型(HMT)的基础上给出了一种图像去噪新方法。实验结果表明,与普通的小波去噪方法相比,该方法不但可以保留图像的边缘信息,而且能提高去噪后图像的峰值信噪比。

Wavelet image denoising has been well acknowledged as an important method of image denoising, Although it can preserve edge information, present methods ignore relativity of wavelet coefficients. According to the deficiency, a new image denoising method was proposed based on hidden markov tree. Experimental results showed that, compared with the usual denoising method, the proposed method could keep images edges from damaging and increase PSNR.