基于四元数小波变换域统计模型的图像去噪

Image Denoising Using Statistical Model Based on Quaternion Wavelet

图像去噪是图像处理的基本问题,四元数小波变换是1种新的多尺度分析工具。图像经四元数小波变换后,其小波系数不仅在尺度间具有相关性,而且在尺度内也具有一定的相关性。首先利用层内及层间的相关性,用非高斯分布对四元数小波系数进行建模,然后给出分类准则,把小波系数分类为重要系数和不重要系数,再用非高斯分布模型对重要系数与其邻域系数进行建模,最后运用最大后验估计原图像的小波系数,从而达到去除图像噪声的目的。仿真实验表明,文中方法不仅可以获得较高的峰值信噪比,而且在视觉上达到明显的去噪效果。

Image denoising is the basic problem of image processing. Quaternion wavelet transform is a new kind of multiresolution analysis of image processing tools. Image via quaternion wavelet transform, wavelet coefficients both in intrascale and in interscale have certain correlations. Firstly, non -Gaussian distribution model is used to describe the correlation of quaternion wavelet coefficients in interscale and intrascale, and classify important coefficients and unimportant coefficients. Then the Gaussian distribution model is used to model the important coefficients and its adjacent coefficients and the MAP estimate original image wavelet coefficients from noisy coefficients, so as to achieve the purpose of denoising. The experiment results show that this method not only gets high peak signal-to-noise ratio（ PSNR）, but also obtains better visual quality.