结构相似性灰关联在强噪声图像增强中的应用

Research on image enhancement for the heavy noised image based on structural similarity via gray relational analysis

为提高强噪声环境下的图像质量,提出一种图像增强新算法.该算法首先对含噪图像进行多尺度小波分解,得到不同尺度、不同方向下的频域信息,然后利用图像中噪声与边缘在不同频带上的分布规律和衰减特性,通过灰色理论中的灰色关联度来区分噪声与边缘,从而在噪声抑制和边缘增强两个方面提高图像的质量.实验结果初步显示,与传统的空域滤波方法和相对较新的小波自适应阈值去噪、Contourlet域自适应阈值去噪等方法相比较,新算法所得图像的视觉效果得到了改善,峰值信噪比最优,可用于强噪声环境下的图像增强预处理.

To improve the heavy noised image quality,this paper proposes a new image enhancement algorithm.Firstly,the noised image is transformed into the wavelet domain via multiscale decomposition,and the information in different resolutions and different directions in the frequency domain are obtained.Then the regularities of coefficient distribution and attenuation characteristics of noise and edges in different subbands are employed to distinguish noise from edges and textures,and thus the image quality is improved by noise suppression and edge enhancement.Experimental results show that our algorithm is superior to most traditional spatial filters and new-developed methods based on the wavelet or contourlet transform both in visual effect and Peak-Signal-to-Noise Ratio（PSNR）.It can be applied to image enhancement preprocessing in heavy noise environment.