[1]郑云海,郭建钊,王门鸿,等.基于人眼视觉特性的Curvelet域低照度图像增强[J].应用科技,2018,45(06):27-31.[doi:10.11991/yykj.201712018]
 ZHENG Yunhai,GUO Jianzhao,WANG Menhong,et al.Low-light image enhancement in Curvelet domain based on human visual characteristics[J].Applied science and technology,2018,45(06):27-31.[doi:10.11991/yykj.201712018]
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基于人眼视觉特性的Curvelet域低照度图像增强(/HTML)
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《应用科技》[ISSN:1009-671X/CN:23-1191/U]

卷:
第45卷
期数:
2018年06期
页码:
27-31
栏目:
现代电子技术
出版日期:
2018-11-05

文章信息/Info

Title:
Low-light image enhancement in Curvelet domain based on human visual characteristics
作者:
郑云海1 郭建钊1 王门鸿1 李佳2 李庆武2
1. 国网泉州供电公司, 福建 泉州, 362000;
2. 河海大学 物联网工程学院, 江苏 常州, 213022
Author(s):
ZHENG Yunhai1 GUO Jianzhao1 WANG Menhong1 LI Jia2 LI Qingwu2
1. State Grid Quanzhou Electric Power Supply Company, Quanzhou 362000, China;
2. College of Internet of Things Engineering, Hohai University, Changzhou 213022, China
关键词:
图像处理图像增强低照度图像CurveletHSI空间人眼视觉特性亮度遮蔽亮度-对比度遮蔽
Keywords:
image processingimage enhancementlow-light imageCurveletHIS spacehuman visual characteristicsluminance maskingluminance and contrast masking
分类号:
TP394.1
DOI:
10.11991/yykj.201712018
文献标志码:
A
摘要:
针对低照度下图像降质严重的问题,提出了一种基于人眼视觉特性和Curvelet变换的低照度图像增强算法。首先将低照度图像转换至“色调-饱和度-亮度”(HSI)颜色空间,在Curvelet域中分解亮度参量得到不同尺度、不同方向的子带分量,以此构建人眼视觉模型;然后利用模型的亮度遮蔽特性和亮度-对比度遮蔽特性对高频分量进行非线性增强,同时对低频分量进行非线性拉伸;最后通过Curvelet逆变换重构亮度参量,结合原始图像的色度和饱和度分量将图像转换至原色彩空间,得到增强后的低照度图像。实验结果表明,该算法可以有效提升低照度图像的对比度和亮度,保持图像的细节信息,抑制图像噪声。
Abstract:
Aiming at the serious degradation of images under low illumination, this paper proposes a low-light image enhancement method based on human visual characteristics and Curvelet transform. Firstly, transform the low-light image into the hue-saturation-intensity (HSI) space, and obtain the sub-band components of different scales and directions in Curvelet domain, then construct the human visual model on this basis; and further enhance the high-frequency components nonlinearly based on the luminance masking and luminance-contrast masking characteristic of the model, and at the same time, stretch the low-frequency components nonlinearly. Finally, reconstruct the brightness components by the inverse Curvelet transform. After the combination with the hue and saturation components, convert the image to the original color space to obtain the enhanced low-lightness image. Experimental results demonstrate that the algorithm can effectively enhance the contrast and brightness of low-light images, keep the details of images and suppress the noise of images.

参考文献/References:

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备注/Memo

备注/Memo:
收稿日期:2017-12-30。
基金项目:国家自然科学基金项目(41706103);国家电网科技项目(52133016000U)
作者简介:郑云海(1979-),男,高级工程师
通讯作者:李庆武,E-mail:li_qingwu@163.com
更新日期/Last Update: 2018-11-02