基于视觉特性的非锐化掩模图像增强  

An Unsharp-mask Image Enhancement Algorithm Based on Human Visual System

作　　者：李成[1] 鞠明[1] 毕笃彦[1] 徐建军[1] 

机构地区：[1]空军工程大学工程学院信号与信息处理实验室,西安710038

出　　处：《光电工程》2009年第9期110-117,共8页Opto-Electronic Engineering

基　　金：国家863计划(2007AA701121)

摘　　要：将人类视觉特性中灰度敏感程度和颜色相关性引入到图像增强中。选择HSV彩色空间,提出最优可见偏差(OND)引导的非锐化掩模(UM)图像增强算法(OND-UM)对亮度分量进行边缘和细节增强,其中通过数值拟合得到了OND曲线,并构造了基于局部方差的自适应增益函数以实现对不同细节区域增强强度的自适应调整;尤其针对含有低对比度区域图像,利用Otsu分割方法确定阈值的分段自适应拉伸函数进行图像的全局调整和对比度增强;而饱和度分量进行直方图均衡,色度分量保持不变;颜色空间转换后再进行基于改进的颜色相关性统计的RGB空间颜色校正。仿真结果表明,OND-UM对连续边缘增强和边缘噪声抑制方面较优,而颜色校正后的图像色彩更丰富艳丽,更符合人眼视觉。The characteristics of Human Visual System （HVS） on noticeable brightness difference and correlation properties in natural color images are brought into the image enhancement algorithm. Based on the HSV color space, a novel Unsharp Mask （UM） image enhancement method guided by Optimum Noticeable Difference （OND）, called OND-UM, is proposed for the V component to bring about the edge and detailed information. The key procedures include that the OND curve is obtained by the numerical fitting technique and an adaptive gain function is designed with local variance. Especially, for the low-contrast regions, the proposed Adaptive Stretching Function （ASF） improves the global contrast. And the S component is histogram-equalized and H component remains the same. At last, the color restoration is conducted in RGB space on statistic data of color correlation properties. Experimental results show good performance of the OND-UM to enhance the edge and reduce noise sensitivity, and the enhanced images after the color restoration look more natural and vivid and are suitable for the human vision.

关 键 词：非锐化掩模 最优可见偏差 视觉特性 自适应拉伸函数 

分 类 号：TP391.4[自动化与计算机技术—计算机应用技术] TN911.73[自动化与计算机技术—计算机科学与技术]