[1]代少升,李东阳,聂合文,等.基于自适应分数阶微分的红外目标增强算法[J].红外技术,2020,42(3):257-263.[doi:10.11846/j.issn.1001_8891.202003008]
 DAI Shaosheng,LI Dongyang,NIE Hewen,et al.Linear Enhancement Algorithm of Infrared Target Based on Adaptive Fractional Differentiation[J].Infrared Technology,2020,42(3):257-263.[doi:10.11846/j.issn.1001_8891.202003008]
点击复制

基于自适应分数阶微分的红外目标增强算法
分享到:

《红外技术》[ISSN:1001-8891/CN:CN 53-1053/TN]

卷:
42卷
期数:
2020年第3期
页码:
257-263
栏目:
出版日期:
2020-03-23

文章信息/Info

Title:
Linear Enhancement Algorithm of Infrared Target Based on Adaptive Fractional Differentiation

文章编号:
1001-8891(2020)05-0257-07
作者:
代少升李东阳聂合文姚俐
重庆邮电大学 通信与信息工程学院
Author(s):
DAI ShaoshengLI DongyangNIE HewenYAO Li
School of Communication and Information Engineering, Chongqing University of Posts and Telecommunications

关键词:
红外图像目标增强自适应分数阶微分线性变换局部目标背景比
Keywords:
infrared image target enhancement adaptive fractional differentiation linear transformation local target-to-background ratio
分类号:
TP391
DOI:
10.11846/j.issn.1001_8891.202003008
文献标志码:
A
摘要:
针对红外图像存在灰度范围窄、图像细节不清晰、目标边缘模糊的问题,提出了一种基于自适应分数阶微分的红外目标增强方法。该方法首先利用图像的梯度、信息熵进行有效融合,并且自适应调整分数阶微分以增强图像中的目标边缘;然后采用图像像素灰度的标准差和均值进行融合去确定目标的分割阈值,以区分出图像中的背景和目标部分;通过对图像中的目标区域进行线性增强,以进一步突显目标。经过实验验证:本文提出的方法能够有效地区分红外图像中的目标和背景,局部目标背景比(Target-to-Background Ratio,TBR)平均提高了0.5,视觉效果比较理想。
Abstract:
To solve the problems associated with infrared images, such as narrow gray range, unclear image details and fuzzy target edge, an infrared target enhancement method based on adaptive fractional differentiation is proposed. In this method, first, the gradient and information entropy of image are used for effective fusion, and the fractional differentiation is adaptively adjusted to enhance the edge of the target in the image. Subsequently, the standard deviation and mean value of the image pixel gray are fused to determine the segmentation threshold of the target, to distinguish the background and target in the image. The target area of the image is linearly enhanced to better highlight the target. Experimental results show that the proposed method can effectively distinguish the target and background in the infrared image. The average local target-to-background ratio (TBR) increased by 0.5, and the visual effect was ideal.

参考文献/References:

[1] 吴旭景, 杜斌. 红外热成像无损检测技术现状及发展的相关研究[J]. 化工管理, 2018(29): 183.
WU X J, DU B. Research on the status and development of Infrared Thermal Imaging Nondestructive Testing Technology[J]. Chemical Management, 2018(29): 183.
[2] 王好贤, 董衡, 周志权. 红外单帧图像弱小目标检测技术综述[J]. 激光与光电子学进展, 2019, 56(8): 9-22.
WANG H X, DONG H, ZHOU Z Q. Review on dim small target detection technologies in infrared single frame image[J]. Laser & Optoelectronics Progress, 2019, 56(8): 9-22.
[3] 易诗, 张洋溢, 聂焱, 等. 红外图像中快速运动目标的检测与跟踪方法[J]. 红外技术, 2019, 41(3): 268-272.
YI Shi, ZHANG Yangyi, NIE Yan, et al. Fast moving target detection and tracking method in infrared image[J]. Infrared Technology, 2019, 41(3): 268-272.
[4] 张丽. 对比度受限自适应直方图均衡方法[J]. 电脑知识与技术, 2010, 6(9): 2238-2241.
ZHANG L. Contrast limited adaptive histogram equalization[J]. Computer Knowledge and Technology, 2010, 6(9): 2238-2241.?
[5] 栾孟杰. 一种多分辨多尺度的红外图像增强算法[J]. 激光杂志, 2019, 40(8): 81-84.
LUAN M J. A multiresolution and multiscale infrared image enhancement algorithm[J]. Laser Journal, 2019, 40(8): 81-84.
[6] QI S X, MA J, LI H, et al. Infrared small target enhancement via phase spectrum of Quaternion Fourier Transform[J]. Infrared Physics and Technology, 2014, 62: 50-58.
[7] 牛为华, 孟建良, 崔克彬, 等. 利用Grümwald-Letnikov分数阶方向导数的图像增强方法[J]. 计算机辅助设计与图形学学报, 2016, 28(1): 129-137.
NIU W H, MENG J L, CUI K B, et al. Image enhancement method using Grümwald-Letnikov fractional directional derivative[J]. Journal of Computer-Aided Design & Computer Graphics, 2016, 28(1): 129-137.
[8] 刘德全, 崔涛, 杨雅宁. 局部对比度自适应直方图均衡化图像增强的算法研究[J]. 信息与电脑(理论版), 2016(7): 79-80.
LIU D Q, CUI T, YANG Y N. Algorithm Research on local contrast adaptive histogram equalization image enhancement[J]. China Computer & Communication (theoretical edition), 2016(7): 79-80.
[9] 王瑞. 小波变换在红外图像处理中的应用研究[D]. 淮南: 安徽理工大学, 2016.
WANG R. The application of wavelet transform in infrared image processing[D]. Huainan: Anhui University of technology, 2016.

相似文献/References:

[1]郭水旺,王宝红,季钢,等.基于基因表达式编码算法的红外图像轮廓提取[J].红外技术,2013,35(01):038.
 GUO Shui-wang,WANG Bao-hong,JI Gang,et al. Infrared Image Contour Extraction Based on the Gene Expression Coding Algorithm[J].Infrared Technology,2013,35(3):038.
[2]孙爱平,皮冬明,安长亮,等. 光机装校阶段红外与可见光图像配准技术研究[J].红外技术,2013,35(01):050.
 SUN Ai-ping,PI Dong-ming,AN Chang-liang,et al. Study on IR/Visible Image Registration for Lens Assembly[J].Infrared Technology,2013,35(3):050.
[3]路建方,王新赛,肖志洋,等. 基于FPGA的红外图像自适应分段线性增强算法[J].红外技术,2013,35(02):102.
 LU Jian-fang,WANG Xin-sai,XIAO Zhi-yang,et al. An Adaptive Piecewise Linear Enhance Algorithm for Infrared Image Based on FPGA[J].Infrared Technology,2013,35(3):102.
[4]徐铭蔚,李郁峰,陈念年,等.多尺度融合与非线性颜色传递的微光与红外图像染色[J].红外技术,2012,34(12):722.
 XU Ming-wei,LI Yu-feng,CHEN Nian-nian,et al. Coloration of the Low Light Level and Infrared Image Using Multi-scale Fusion and Nonlinear Color Transfer Technique[J].Infrared Technology,2012,34(3):722.
[5]纪利娥,杨风暴,王志社,等. 基于边缘图像和SURF特征的可见光与红外图像的匹配算法[J].红外技术,2012,34(11):629.
 JI Li-e,YANG Feng-bao,WANG Zhi-she,et al.Visible and Infrared Image Matching Algorithm Based on Edge Image and SURF Features[J].Infrared Technology,2012,34(3):629.
[6]张红辉,罗海波,余新荣,等. 改进的神经网络红外图像非均匀性校正方法[J].红外技术,2013,35(04):232.
[7]张强,侯宁,刘红燕. 红外焦平面阵列非均匀性多点实时压缩校正研究[J].红外技术,2012,34(10):593.
 ZHANG Qiang,HOU Ning,LIU Hong-yan. Study on Real-time Multi-points Compressive Nonuniformity Correction of IRFPA[J].Infrared Technology,2012,34(3):593.
[8]路建方,王新赛,肖志洋,等. 基于灰度分层的FPGA红外图像伪彩色实时化研究[J].红外技术,2013,35(05):285.
 LU Jian-fang,WANG Xin-sai,XIAO Zhi-yang,et al. The Research on Real-time Pseudo-color of Infrared Image in FPGA Based on Gray Delaminating[J].Infrared Technology,2013,35(3):285.
[9]陈钱.红外图像处理技术现状及发展趋势[J].红外技术,2013,35(06):311.
 CHEN Qian.The Status and Development Trend of Infrared Image Processing Technology[J].Infrared Technology,2013,35(3):311.
[10]谭东杰,张安.基于局部直方图规定化的红外图像非均匀性校正[J].红外技术,2013,35(06):325.
 TAN Dong-jie,ZHANG An.Non-uniformity Correction Based on Local Histogram Specification[J].Infrared Technology,2013,35(3):325.
[11]张麟瑞,贾玉林,程科.基于信息量的海上红外目标增强[J].红外技术,2006,28(2):108.
 ZHANG Lin-rui,JIA Yu-lin,CHENG Ke.Information Capacity-based Enhancement of Infrared Targets on The Sea[J].Infrared Technology,2006,28(3):108.

备注/Memo

备注/Memo:
收稿日期:2019-10-31;修订日期:2020-03-06.
作者简介:代少升(1974-),男,河南潢川人,教授,主要从事红外图像处理方向的研究。E-mail: daiss@cqupt.edu.com。?
基金项目:国家自然科学基金(61671094);重庆市科学技术委员会国家科学基金(CSTC2015JCYJA40032)。

更新日期/Last Update: 2020-03-17