|本期目录/Table of Contents|

[1]杨爱萍,宋曹春洋,张莉云,等.基于透射率归一化的弱光图像增强[J].天津大学学报(自然科学版),2017,(09):997-1003.[doi:10.11784/tdxbz201607033]
 Yang Aiping,Song Caochunyang,Zhang Liyun,et al.Low-Light Image Enhancement Based on Transmission Normalization[J].Journal of Tianjin University,2017,(09):997-1003.[doi:10.11784/tdxbz201607033]
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基于透射率归一化的弱光图像增强()
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《天津大学学报(自然科学版)》[ISSN:0493-2137/CN:12-1127/N]

卷:
期数:
2017年09
页码:
997-1003
栏目:
电气自动化与信息工程
出版日期:
2017-09-22

文章信息/Info

Title:
Low-Light Image Enhancement Based on Transmission Normalization
文章编号:
0493-2137(2017)09-0997-07
作者:
杨爱萍 宋曹春洋 张莉云 白煌煌 卜令勇
天津大学电气自动化与信息工程学院,天津 300072
Author(s):
Yang Aiping Song Caochunyang Zhang Liyun Bai Huanghuang Bu Lingyong
School of Electrical and Information Engineering, Tianjin University, Tianjin 300072, China
关键词:
图像增强 点暗原色 透射率 归一化 维纳滤波
Keywords:
image enhancement point dark channel transmission normalization Wiener filtering
分类号:
TP751.1
DOI:
10.11784/tdxbz201607033
文献标志码:
A
摘要:
针对阴天或夜晚等弱光条件下拍摄的图像具有低信噪比、低对比度、强噪声等问题, 在暗原色先验理论去雾框架下, 提出了一种基于透射率归一化的弱光图像增强方法.首先, 针对弱光图像的特点, 简化弱光图像增强模型; 然后, 用点暗原色取代块暗原色求取透射率, 并利用局部维纳滤波进行细化, 以保留更多图像的细节; 最后, 由弱光图像直接对透射率进行归一化即可得到增强图像.实验结果表明, 该方法运算简单, 可有效增强弱光图像, 且能保留更多的图像细节.
Abstract:
The pictures taken in low-light environment such as on a cloudy day or at night have very low SNR,low contrast and strong noise. To solve the problem,under the haze removal framework of dark channel prior theory,a low-light image enhancement method based on transmission normalization is proposed. First,according to the characteristics of low-light image,the model of low-light image enhancement is simplified. Then the transmission is calculated by point dark channel instead of block dark channel and the transmission is further detailed by local Wiener filter in order to retain more image details. Finally,The transmission is directly normalized by the low-light image to get the enhanced image. Experimental results demonstrate that the proposed method is effective not only in needing little computation to get the enhanced image,but also in retaining more image details.

参考文献/References:

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备注/Memo

备注/Memo:
收稿日期: 2016-07-10; 修回日期: 2016-11-18.
作者简介: 杨爱萍(1977—), 女, 博士, 副教授.
通讯作者: 杨爱萍, yangaiping@tju.edu.cn.
基金项目: 国家自然科学基金资助项目(61372145).
Supported by the National Natural Science Foundation of China(No. 61372145).
更新日期/Last Update: 2017-09-10