基于粗糙集与小波反锐化掩模的图像增强

为了有效去除图像中的干扰噪音,改善模糊不清晰的有用边缘细节信息,提高图像的层次感和清晰度,提出将粗糙集和小波反锐化掩模相结合的方法来实现图像增强.运用粗糙集中的近似及等价属性关系将知识化后的图像划分成不同的区域,再根据估计好的阈值进行数据约简,实现有效去除噪音.运用小波反锐化掩模法对图像的轮廓及细节信息进行增强处理,完成图像的最终增强.实验结果表明,不论在主观的视觉效果上,还是客观的噪音均方误差,该方法的处理结果都是较为理想的.

Image Enhancement Based on Rough Sets and Wavelet Unsharp Masking

In order to remove the disturbing noise embedded in the image,protect the edge and useful detail information,and improve the visual contrast effect,a new method for image enhancement based on rough sets and wavelet unsharp masking is presented.The original image being knowledgeable could be divided into different regions by approximate and equivalent relations.The noise effectively with the debugged threshold is removed.The image enhancement after using wavelet unsharp masking to improve the edge and useful detail information are studied.Experiment results show that this new method is rather idea not only in object visual effect,but also in subject signal-noise ration.