自适应直觉模糊相异直方图裁剪的图像增强算法
作者:
作者单位:

西安邮电大学

作者简介:

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中图分类号:

TP391

基金项目:

国家自然科学基金项目(61571361,61671377),西安邮电大学西邮新星团队计划(xyt2016-01)


Adaptive intuitionistic fuzzy dissimilar histogram clipping image enhancement algorithm
Author:
Affiliation:

Xi''an University of Posts & Telecommunications

Fund Project:

The National Natural Science Foundation of China (General Program, Key Program, Major Research Plan),the New Star Team of Xi’an University of Posts & Telecommunications of China

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    摘要:

    针对经典的直方图均衡化图像增强算法可能存在的对比度过度增强、亮度分布不均匀和细节信息不突出等问题,提出了自适应直觉模糊相异直方图裁剪的图像增强算法. 基于直觉模糊集的“投票模型”,本文引入直觉模糊相异直方图的概念,并基于此提取图像像素的空间位置信息. 同时,利用S型隶属度函数对图像的直觉模糊相异直方图进行自适应裁剪,采用分段策略对裁剪后的直觉模糊相异直方图进行均衡化处理. 最后,利用直觉模糊集的犹豫度刻画原图像的未知信息,修正由引导滤波获得的细节图像,从而保留图像丰富的细节信息. 针对三种类型的图像,即,自然图像、MRI脑图像及近红外图像的实验结果表明,本文算法能有效提高图像的对比度,保留图像的细节信息,使图像呈现较自然的视觉效果,改善图像的质量评价指标.

    Abstract:

    Aiming at the shortcomings of classic histogram equalization image enhancement algorithm, such as excessive contrast, uneven brightness distribution and low detail information, an adaptive intuitionistic fuzzy dissimilar histogram clipping image enhancement algorithm is proposed. Firstly, based on the ``voting model" of intuitionistic fuzzy set, this paper defines the concept of intuitionistic fuzzy dissimilar histogram to extract the spatial location information of image pixels. Secondly, an S-type membership function is used to adaptively clip the intuitionistic fuzzy dissimilar histogram. Then, a segmentation strategy is used to equalize the clipped intuitionistic fuzzy dissimilar histogram. Finally, the hesitancy of the intuitionistic fuzzy set is used to characterize the unknown information of the original image, and modify the detailed image obtained by the guide filtering, thereby the rich detailed information of an image is retained. The experimental results for three types of images, namely natural images, MRI brain images and near-infrared images, show that the proposed algorithm efficiently enhances contrast, retains the details, yields natural visual quality images, and improves an image quality evaluation index.

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  • 收稿日期:2020-06-27
  • 最后修改日期:2020-09-11
  • 录用日期:2020-09-25
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