一种基于高阶统计量的图象平滑去噪法

为了消除或衰减存在于图象上的噪声，同时尽可能地保留图象细节，提出了一种基于高阶统计量的图象平滑去噪法，该方法是根据高阶统计量对高斯噪声不敏感的特性，对一个像素点采用其周围梯度和最小的几个点的灰度平均值来代替其灰度值，以便可以在对噪声进行平滑滤波的同时，最大限度地保留图象的细节信息，仿真结果表明，该方法较好地实现了这一要求，与常用的中值滤波法相比，该方法处理后的图象，其PSNR可提高约1dB以上。

A Novel Image Noise Smoothing Method Based on High Order Statistics

Informations are playing more and more important role in our life. Most of them are images. However, many kinds of noise will occur during image generation, communication and transformation, and will affect the image quality. By using image-smoothing technique, the noise can be dwindled, but some image details and value information are also lost while the dwindling noise by using the most used traditional smoothing methods. For solving this problem, a novel image noise smoothing method based on high order statistics has been proposed in this paper. It uses the property that high order statistics are not sensitive to Gauss noise to process image-smoothing, and can mostly save the image details while greatly dwindling noise. Experimental results have shown the effectiveness of the proposal scheme, the experimental image quality has been improved about 1.7dB compared with the image processed by other method.