摘要
针对传统Beta函数的图像增强方法应用于较暗区域、较亮区域,以及两端区域拉伸与中间压缩图像增强中的不足,引入增强算子和新的形状控制参数,提出了新的直方图变换函数,即广义Beta函数。在此基础上,给出了广义Beta函数增强算子选择机制,并建立了新形状控制参数的自适应选取模型。实验结果表明,提出的基于广义Beta函数的图像增强方法不仅增强了图像的对比度,而且保留了较多的图像细节信息,增强结果明显优于传统Beta函数。
Aiming at the shortcoming of image enhancement method based on traditional function when applied in the sketching of dark area, bright area and areas at both ends as well as the compressing of centre area, this paper presented a novel histogram transforming function, i.e. extended Beta function, by introducing enhancement operator and new shape-controlling parameters. On this basis, put forward the selection mechanism of enhancement operator of extended Beta, and established the self-adaptive selection model of the new shape-controlling parameters. The experimental results show that the contrast of image is improved as well as the details of image is retained by the presented method with obviously better performance than traditional Beta function.
出处
《计算机应用研究》
CSCD
北大核心
2011年第12期4742-4745,共4页
Application Research of Computers
基金
国家"863"高技术研究发展计划资助项目(2006AA04A124)
国家教育部博士点专项基金资助项目(20090191110022)
关键词
图像对比度增强
变换函数
量子遗传算法
直方图
image contrast-enhancement
transforming function
quantum genetic algorithm
histogram
