基于离群点检测的图形图象噪声滤除算法

图形图象噪声过滤与修正,在媒体制作、图象分析与信息提取中起着十分重要的作用.虽然基于小波变换的算法能够对高斯噪声进行较好的滤噪处理,但对于随机分布于图象中的各种非高斯噪声仍没有普遍适用的滤噪方法.为了对这种随机分布于图象中的噪声进行有效的检测与滤除,采用对数字图象像素进行解析化描述的方法,从离群点检测的角度给出噪声的定义,并在此基础上构造了相应的图象噪声检测与滤除算法.实验结果表明,这一新方法对图象类型具有广泛的适应性和较好的噪声滤除效果,在大规模图形图象处理应用中具有实用价值.

Image Denoising Via Outlier Pixel Detection

Image denoising plays an important role in various image-related applications. While serials of wavelet-based denoising schemes fit well to images with Gaussian (white) noise, few of them can handle images with various non-Gaussian noises effectively. This paper deals with the problem from the data mining approach. It treats noisy pixels in an image as isolating outliers that are discernible in color attributes from their neighbor pixels. Inspired by the idea from outlier detection analysis, it first maps the pixels of the image into a metric space and then introduces a distance among the pixels. By making use of the density function on the pixel data set, it formulates an analytical definition of the noisy point. Further, the paper discusses the properties of the non-noisy points and constructs a denoising algorithm. Results of experiments and real world applications show that this novel approach is effective both to Gaussian and non-Gaussian noise. The method can be implemented for mass image denoising with satisfactory efficiency and denoising quality.