颜色对比度增强的红外与可见光图像融合方法

曾祥通; 张玉珍; 孙佳嵩; 喻士领

留言板

尊敬的读者、作者、审稿人, 关于本刊的投稿、审稿、编辑和出版的任何问题, 您可以本页添加留言。我们将尽快给您答复。谢谢您的支持!

姓名
邮箱
手机号码
标题
留言内容
验证码

颜色对比度增强的红外与可见光图像融合方法

曾祥通, 张玉珍, 孙佳嵩, 喻士领

文章导航 > 红外与激光工程 > 2015 > 44(4): 1198-1202

曾祥通, 张玉珍, 孙佳嵩, 喻士领. 颜色对比度增强的红外与可见光图像融合方法[J]. 红外与激光工程, 2015, 44(4): 1198-1202.

引用本文:

曾祥通, 张玉珍, 孙佳嵩, 喻士领. 颜色对比度增强的红外与可见光图像融合方法[J]. 红外与激光工程, 2015, 44(4): 1198-1202.

Zeng Xiangtong, Zhang Yuzhen, Sun Jiasong, Yu Shiling. One color contrast enhanced infrared and visible image fusion method[J]. Infrared and Laser Engineering, 2015, 44(4): 1198-1202.

Citation:

Zeng Xiangtong, Zhang Yuzhen, Sun Jiasong, Yu Shiling. One color contrast enhanced infrared and visible image fusion method[J]. Infrared and Laser Engineering, 2015, 44(4): 1198-1202.

颜色对比度增强的红外与可见光图像融合方法

曾祥通^1,2,
张玉珍^1,2,
孙佳嵩^1,2,
喻士领^1,2

1.
南京理工大学电子工程与光电技术学院,江苏南京 210094;
2.
江苏省光谱成像与智能感知重点实验室,江苏南京 210094

基金项目:

国家自然科学基金(6177091)

详细信息

作者简介:
曾祥通(1989-),男,硕士生,主要从事图像融合和目标识别与跟踪方面的研究.Email:zengxt2013@126.com

中图分类号: TP391

One color contrast enhanced infrared and visible image fusion method

1.
School of Electronic and Optical Engineering,Nanjing University of Science and Technology,Nanjing 210094,China;
2.
Jiangsu Key Laboratory of Spectral Imaging and Intelligent Sense,Nanjing 210094,China

摘要: 针对红外与可见光彩色融合图像中目标与背景间的低对比度的问题,提出一种基于HSI空间颜色对比度增强的红外和可见光图像融合方法.首先对输入的可见光与红外图像进行直方图均衡和中值滤波加强处理,然后对加强的红外图像模糊阈值分割得到红外目标,最后把分割的红外目标图像和加强的可见光和红外图像在HSI空间的三通道线性融合和色彩传递,为了增强目标与背景间的颜色对比度,在色彩传递阶段, H通道的色彩传递方程中引入一个比例因子.实验结果表明：与其他算法相比,该方法得到的彩色融合图像热目标和低温物体与背景间的颜色对比度明显加强,同时背景的细节信息呈现白天类似的自然彩色,更加符合人眼视觉感知.
- 颜色对比度增强 /
- 图像融合 /
- HSI空间 /
- 色彩传递
Abstract: Focus on color fusion for infrared and visible images with low contrast between target and background issues, a contrast enhancement method for infrared and visible image was presented based on HSI color space. Firstly, the contrast of infrared and visible images was enhanced using local histogram equalization and median filter, then the infrared target was extracted from the enhanced infrared image after fuzzy threshold segmentation. Finally, the two enhanced images and the segmentation infrared target were fused into the three components of a HSI image in terms of a simple linear fusion strategy and color transfer. To enhance the color contrast between the target and the background, a scaling factor was introduced into the transferring equation in the H channel during color transfer process. The experimental results show that, compared with other algorithms, the color fusion images of hot target and color contrast between low temperature object and background abtained by the presented method enhance obviously. At the same time, the details of visible images are endowed with natural color similar to that of the light color images during the day, which is more confortable to the human's visual perception.
- color contrast enhancement /
- image fusion /
- HSI space /
- color transfer

[1]	Toet A. Applying daytime colors to multiband nightvision imagery[C]//SPIE, 2003: 168-178.
[2]
[3]	Toet A. Natural color mapping for multiband nightvision imagery[J]. Information Fusion, 2003, 4(2): 155-166.
[4]
[5]
[6]	Wang L X, Shi S M, Jin W Q, et al, Color fusion algorithm for visible and infrared images based on color transfer in YUV color space[C]//SPIE, 2007: 1781-1787.
[7]	Zheng Y, Essock E A. A local-coloring method for night-vision colorization utilizing image analysis and fusion[J].Information Fusion, 2008, 9(2): 186-199.
[8]
[9]	Yin S F, Cao L C, Ling Y S, et al. One contrast enhancement method for color night vision[J]. Infrared Physics Technology, 2010, 53(4): 146-150.
[10]
[11]
[12]	Qian X Y, Wang Y J, Wang B F. Effective contrast enhancement method for color night vision[J]. Infrared Physics and Technology, 2012, 55(1): 130-136.
[13]	Qian X Y, Wang Y J, Wang B F. Color contrast enhancement for color night vision based on color mapping[J]. Infrared Physics and Technology, 2013, 57(6): 36-41.
[14]
[15]	Jiang Yiming, Wang Keyong. Infrared image segmentation algorithm of tank[J]. Infrared and Laser Engineering, 2007, 56(4): 1481-1485. (in Chinese)
[16]
[17]
[18]	Cheng Guo, Zuo Hongfu. The image adaptive thresholding by index of fuzziness[J]. Acta Automatic Sinica, 2003, 29(5): 386-390. (in Chinese)
[19]
[20]	Jang J H, Ra J B. Psehdo-color image based on intensity-hue- saturation[C]//Proceedings of IEEE International Conference on Multisensor Fusion and Itegration for Intelligent Systems, 2008: 1066-1071.
[21]	Cheng Zhengyun, Wang Xia, Zou Xiaofeng, et al. Polarimetric and milti-spectral image fusion based on HIS color system and wavelet transform[J]. Acta Photonica Sinica, 2010, 39(1): 1710-1712. (in Chinese)

[1]	宦克为, 李向阳, 曹宇彤, 陈笑. 卷积神经网络结合NSST的红外与可见光图像融合 . 红外与激光工程, 2022, 51(3): 20210139-1-20210139-8. doi: 10.3788/IRLA20210139
[2]	闵莉, 曹思健, 赵怀慈, 刘鹏飞. 改进生成对抗网络实现红外与可见光图像融合 . 红外与激光工程, 2022, 51(4): 20210291-1-20210291-10. doi: 10.3788/IRLA20210291
[3]	谭威, 宋闯, 赵佳佳, 梁欣凯. 基于多层级图像分解的图像融合算法 . 红外与激光工程, 2022, 51(8): 20210681-1-20210681-9. doi: 10.3788/IRLA20210681
[4]	高红霞, 魏涛. 改进PCNN与平均能量对比度的图像融合算法 . 红外与激光工程, 2022, 51(4): 20210996-1-20210996-8. doi: 10.3788/IRLA20210996
[5]	沈英, 黄春红, 黄峰, 李杰, 朱梦娇, 王舒. 红外与可见光图像融合技术的研究进展 . 红外与激光工程, 2021, 50(9): 20200467-1-20200467-18. doi: 10.3788/IRLA20200467
[6]	金哲彦, 徐之海, 冯华君, 李奇. 基于对焦清晰度的双分辨相机变焦算法研究 . 红外与激光工程, 2020, 49(5): 20190463-20190463-9. doi: 10.3788/IRLA20190463
[7]	曾瀚林, 孟祥勇, 钱惟贤. 高斯差分滤波图像融合方法 . 红外与激光工程, 2020, 49(S1): 20200091-20200091. doi: 10.3788/IRLA20200091
[8]	戴进墩, 刘亚东, 毛先胤, 盛戈皞, 江秀臣. 基于FDST和双通道PCNN的红外与可见光图像融合 . 红外与激光工程, 2019, 48(2): 204001-0204001(8). doi: 10.3788/IRLA201948.0204001
[9]	张家民, 时东锋, 黄见, 王英俭. 图像融合在偏振关联成像中的应用 . 红外与激光工程, 2018, 47(12): 1226002-1226002(7). doi: 10.3788/IRLA201847.1226002
[10]	杨洪飞, 夏晖, 陈忻, 孙胜利, 饶鹏. 图像融合在空间目标三维重建中的应用 . 红外与激光工程, 2018, 47(9): 926002-0926002(8). doi: 10.3788/IRLA201847.0926002
[11]	郭全民, 董亮, 李代娣. 红外与可见光图像融合的汽车抗晕光系统 . 红外与激光工程, 2017, 46(8): 818005-0818005(6). doi: 10.3788/IRLA201746.0818005
[12]	杨桄, 童涛, 孟强强, 孙嘉成. 基于梯度加权的红外与可见光图像融合方法 . 红外与激光工程, 2014, 43(8): 2772-2779.
[13]	王金玲, 贺小军, 宋克非. 采用区域互信息的多光谱与全色图像融合算法 . 红外与激光工程, 2014, 43(8): 2757-2764.
[14]	朴燕, 刘磊, 刘笑宇. 低照度环境下视频增强技术 . 红外与激光工程, 2014, 43(6): 2021-2026.
[15]	张宝辉, 闵超波, 窦亮, 张俊举, 常本康. 目标增强的红外与微光图像融合算法 . 红外与激光工程, 2014, 43(7): 2349-2353.
[16]	杨少魁, 刘文. 一种微光与红外图像彩色融合方法 . 红外与激光工程, 2014, 43(5): 1654-1659.
[17]	毛海岑, 刘爱东. 利用证据理论的图像融合方法 . 红外与激光工程, 2013, 42(6): 1642-1646.
[18]	张勇, 金伟其. 夜视融合图像质量主观评价方法 . 红外与激光工程, 2013, 42(2): 528-532.
[19]	张勇, 金伟其. 夜视融合图像质量客观评价方法 . 红外与激光工程, 2013, 42(5): 1360-1365.
[20]	彭逸月, 何伟基, 顾国华, 童涛. 多层级及对比度提升的红外和可见光图像融合 . 红外与激光工程, 2013, 42(4): 1095-1099.

点击查看大图

计量

文章访问数: 338
HTML全文浏览量: 38
PDF下载量: 338
被引次数: 0

颜色对比度增强的红外与可见光图像融合方法

1. 南京理工大学电子工程与光电技术学院,江苏南京 210094;

2. 江苏省光谱成像与智能感知重点实验室,江苏南京 210094

作者简介:
曾祥通(1989-),男,硕士生,主要从事图像融合和目标识别与跟踪方面的研究.Email:zengxt2013@126.com

收稿日期: 2014-08-12
修回日期: 2014-09-15
刊出日期: 2015-04-25

基金项目:

国家自然科学基金(6177091)

中图分类号: TP391

关键词:

摘要: 针对红外与可见光彩色融合图像中目标与背景间的低对比度的问题,提出一种基于HSI空间颜色对比度增强的红外和可见光图像融合方法.首先对输入的可见光与红外图像进行直方图均衡和中值滤波加强处理,然后对加强的红外图像模糊阈值分割得到红外目标,最后把分割的红外目标图像和加强的可见光和红外图像在HSI空间的三通道线性融合和色彩传递,为了增强目标与背景间的颜色对比度,在色彩传递阶段, H通道的色彩传递方程中引入一个比例因子.实验结果表明：与其他算法相比,该方法得到的彩色融合图像热目标和低温物体与背景间的颜色对比度明显加强,同时背景的细节信息呈现白天类似的自然彩色,更加符合人眼视觉感知.

English Abstract

One color contrast enhanced infrared and visible image fusion method

1. School of Electronic and Optical Engineering,Nanjing University of Science and Technology,Nanjing 210094,China;

2. Jiangsu Key Laboratory of Spectral Imaging and Intelligent Sense,Nanjing 210094,China

Received Date: 2014-08-12
Rev Recd Date: 2014-09-15
Publish Date: 2015-04-25

Keywords:

Abstract: Focus on color fusion for infrared and visible images with low contrast between target and background issues, a contrast enhancement method for infrared and visible image was presented based on HSI color space. Firstly, the contrast of infrared and visible images was enhanced using local histogram equalization and median filter, then the infrared target was extracted from the enhanced infrared image after fuzzy threshold segmentation. Finally, the two enhanced images and the segmentation infrared target were fused into the three components of a HSI image in terms of a simple linear fusion strategy and color transfer. To enhance the color contrast between the target and the background, a scaling factor was introduced into the transferring equation in the H channel during color transfer process. The experimental results show that, compared with other algorithms, the color fusion images of hot target and color contrast between low temperature object and background abtained by the presented method enhance obviously. At the same time, the details of visible images are endowed with natural color similar to that of the light color images during the day, which is more confortable to the human's visual perception.