基于粗糙集的图像平滑研究

由于图像信息本身的复杂性和它们之间有较强的相关性,在处理过程中可能出现不完整性和不确定性问题.粗糙集理论是一种新的处理模糊和不确定性问题的数学工具.本文提出一种粗糙集理论和传统的选择式掩模平滑法相结合的新的图像平滑算法,该算法首先通过像素点的粗糙集近似相似度来判断像素所属的掩模,然后对图像进行平滑处理.该算法利用Matlab编写实现,仿真试验结果表明该算法比传统算法有很大改进,在去除噪声的同时,保持了图像的细节,提高了图像的对比度,质量高于传统的预处理方法处理的图像.

Research on Image Smoothing Method Based on Rough Sets

Due to the complexity and relativity of image information,problems of vagueness and uncertainty rnay emerge when processing it.Rough sets theory is a new mathematical tool to deal with vagueness and uncertainty.The paper presents a new image smoothing algorithm combined rough sets with the traditional selective masking smooth filtering.According to looking for the mask a pixel belongs to,the mask rough sets precision approximation is used to precede image pixel to smooth whole image.This arithmetic is realized by Matlab.Experimental results show that the arithmetic develops the traditional arithmetic,while denoising the method keeps good details,increases the contrast degree of the picture,and improves the picture quality compared with the traditional preprocessing method.