[1]陈珂,柯文德,许波,等.改进的彩色图像去雾效果评价方法[J].智能系统学报编辑部,2015,10(5):803-809.[doi:10.11992/tis.201406003]
 CHEN Ke,KE Wende,XU Bo,et al.An improved assessment method for the color image defogging effect[J].CAAI Transactions on Intelligent Systems,2015,10(5):803-809.[doi:10.11992/tis.201406003]
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《智能系统学报》编辑部[ISSN:1673-4785/CN:23-1538/TP]

卷:
第10卷
期数:
2015年5期
页码:
803-809
栏目:
出版日期:
2015-10-25

文章信息/Info

Title:
An improved assessment method for the color image defogging effect
作者:
陈珂1 柯文德1 许波1 张良均2
1. 广东石油化工学院 计算机科学与技术系, 广东 茂名 525000;
2. 广州太普信息技术有限公司, 广东 广州 510663
Author(s):
CHEN Ke1 KE Wende1 XU Bo1 ZHANG Liangjun2
1. Department of Computer Science and Technology, Guangdong University of Petrochemical Technology, Maoming 525000, China;
2. Guangzhou TipDM Information Technology Co., Ltd., Guangzhou 510663, China
关键词:
图像去雾去雾效果评价大气散射模型相对色彩空间可见边对比度
Keywords:
image defoggingdefogging effect assessmentatmospheric scattering modelopponent color spacecontrast of visible edges
分类号:
TP391
DOI:
10.11992/tis.201406003
文献标志码:
A
摘要:
针对当前对图像去雾效果评价的不足,提出了一种改进的评价彩色图像去雾效果的方法。该方法同时考虑了对图像边缘的评价以及对颜色失真的评价,基于图像雾化的大气散射模型,通过将原始图像转换到相对色彩空间,提出了度量颜色失真的标准;结合对比度增强的评价方式,提出了一个统一的评价指标,从而实现很好地给出一个符合人眼视觉判断的客观评价结果。实验中基于多种去雾算法的去雾结果,对基于可见边比的评估方法、CNC评价指标和本文提出的评价指标进行了对比,结果表明本文改进的评价标准能更好地体现去雾的质量,获得与视觉判定更加接近的结论。
Abstract:
Currently, there is no adequate evaluation method for image defogging effect. Aiming at this, an improved method for the color image defogging effect assessment is proposed. By this method, either the edge’s contrast enhancement or the color distortion is considered. Based on the atmospheric scattering model of an atomized image, the method first converts the original image to the opponent color space, thus deriving a standard for measuring the color distortion. Then, by combining the evaluation means for contrast enhancement, a unified assessment index for color image is generated, with objective evaluation results and good human visual perception. The defogging effects of many defogging methods were evaluated by comparison among the visible edge ratio, CNC value, and the criteria proposed in this paper. The results show that the improved assessment criteria can better reflect defogging quality and is closer to expected visual judgment.

参考文献/References:

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备注/Memo

备注/Memo:
收稿日期:2014-06-05;改回日期:。
基金项目:国家自然科学基金资助项目(61272382);广东省科技计划资助项目(2012B0101100037);广东省高等学校科技创新项目(2013kjcx0132).
作者简介:陈珂,男,1964年生,副教授,主要研究方向为机器学习、数据挖掘和图像处理等。主持广东省科技计划项目2项,广东省教育厅科技创新项目1项。发表学术论文20余篇,其中SCI收录1篇,EI收录9篇;柯文德,男,1976年,教授,博士,主要研究方向机器视觉、人工智能和智能机器人等。主持广东省自然科学基金项目1项,广东省科技计划项目2项。发表学术论文40余篇,其中SCI收录4篇,EI收录20篇;许波,男,1986年,讲师,主要研究方向为计算智能、机器学习。
通讯作者:陈珂.E-mail:chenke2001@163.com.
更新日期/Last Update: 2015-11-16