摘要
本文在模糊增强算法的基础上提出了广义迭代模糊增强算法,它由图像滤波、模糊增强和灰度变换三个步骤组成。在模糊增强阶段,提出了一种规范形式的隶属度函数,在保留了通常模糊增强算法和灰度变换优点的同时,将灰度隶属度函数变换到[0,1]区间。为了约束所提出算法的迭代过程,基于图像灰度直方图分布的统计特性,提出了一种新的客观图像质量评价指标。计算机仿真实例表明,这种新的算法比单一的模糊增强算法和灰度变换算法更适合于处理灰度级少、对比度低的图像的增强问题。
In this paper a generalized iterative fuzzy enhancement algorithm is proposed which consists of a three-stage procedure, i. e. , image filtering, fuzzy enhancement and gray-level transformation. A canonical form of membership function in the stage of fuzzy enhancement is proposed which remains the advantages of the original fuzzy enhancement and the gray level transformation while transforming the membership function of the gray scale to [0,1]. A new objective image quality assessment criterion is suggested on the basis of the statistical features of the gray-level histogram of images to control the iterative procedure of the proposed image enhancement algorithm. Computer simulation results show that this new enhancement method is more suitable than fuzzy enhancement and gray-level transformation for handing the enhancement problems of images with less gray levels and low contrasts.
出处
《模式识别与人工智能》
EI
CSCD
北大核心
2003年第3期338-341,共4页
Pattern Recognition and Artificial Intelligence
关键词
图像增强
图像质量
图像处理
图像识别
图像模糊增强算法
隶属度函数
Image Processing, Fuzzy Enhancement, Generalized Fuzzy Enhancement, Gray Level Transformation, Image Quality Assessment
