Detecting optical component damage based on neighborhood vector dot contrast image enhancement

Zheng-zhou WANG; Ya-xuan DUAN; Li WANG; Meng TAN; Hong-guang LI; Ji-tong WEI

doi:10.3788/OPE.20192712.2668

您当前的位置：

首页 >

文章列表页 >

Detecting optical component damage based on neighborhood vector dot contrast image enhancement

Information Sciences | 更新时间：2020-07-09

- Detecting optical component damage based on neighborhood vector dot contrast image enhancement
- Optics and Precision Engineering Vol. 27, Issue 12, Pages: 2668-2682(2019)
- 作者机构：
  
  中国科学院西安光学精密机械研究所，陕西西安 710119
- 作者简介：
- 基金信息：
- DOI：10.3788/OPE.20192712.2668
  CLC： TP391.1
- Received：13 August 2019，
  
  Accepted：29 August 2019，
  
  Published：25 December 2019
- 稿件说明：
移动端阅览
Zheng-zhou WANG, Ya-xuan DUAN, Li WANG, et al. Detecting optical component damage based on neighborhood vector dot contrast image enhancement[J]. Optics and precision engineering, 2019, 27(12): 2668-2682.
DOI：

Zheng-zhou WANG, Ya-xuan DUAN, Li WANG, et al. Detecting optical component damage based on neighborhood vector dot contrast image enhancement[J]. Optics and precision engineering, 2019, 27(12): 2668-2682. DOI： 10.3788/OPE.20192712.2668.

摘要

为了解决局域对比度方法(LCM)无法检测局部亮区损伤目标和分离效率低的问题，本文提出了基于邻域向量内积局部对比度图像增强的光学元件损伤目标检测方法。首先，将图像中的每一个点的3×3邻域生成一个9维邻域向量，并将邻域内的最大值扩展成一个9维度极值向量，计算邻域向量与极值向量的内积；其次，计算每个像素的邻域向量内积对比度值(NVDC)；然后，计算每个像素的邻域向量内积局部对比度，即在一个较大的区域内(5×5)搜索当前像素所有邻域向量内积对比度的最大值，作为当前像素的邻域向量内积局部对比度值(NVDLC)；最后，对NVDLC图像进行二值化和目标分离。实验结果表明，通过本文的增强方法使得损伤图像的信噪比从3.775提高到12.445，损伤目标信号得到极大的增强。在经过邻域向量内积局部对比度方法图像增强后，能够直接使用自适应阈值公式将小于2 pixel的损伤目标从背景中分离出来，满足了弱对比度损伤目标检测对于精度和效率的要求。

Abstract

To solve the problems of the inability of LCM in detecting damaged targets in local bright areas and its low separation efficiency

a damage detection method based on local contrast image enhancement of a neighborhood vector local dot was proposed in this study. First

a nine-dimensional neighborhood vector was generated from the 3×3 neighborhood of each point in the image

and the maximum value in the neighborhood was extended to the nine-dimensional extreme vector in calculating the dots of the neighborhood vector and extreme vector. Second

the Neighborhood Vector Dot Contrast (NVDC) of each pixel was calculated. Third

the NVDC of the image patch of each pixel was calculated

i.e.

the maximum NVDC value of each pixel in a relatively large area(5×5) was taken as the final Neighborhood Vector Dot Local Contrast (NVDLC) of the corresponding pixel. Fourth

the NVDLC image was binarized

and the targets were separated from the background. The experimental results demonstrate that the LSNR of the damaged image is improved from 3.775 to 12.445

and the signal of the damage target is significantly enhanced using the image enhancement method proposed in this study. After the maximum contrast image of NVDLC is enhanced

the damage targets with size of less than 2 pixels can be directly separated from the background using the adaptive threshold formula

which meets the accuracy and efficiency requirements of the weak contrast damage target detection.

关键词

Keywords

references

彭志涛, 魏晓峰, 元浩宇, 等.全内反射照明光学元件损伤检测信噪比分析[J].红外与激光工程, 2011, 40(6): 1111-1114.

PENG ZH T, WEI X F, YUAN H Y, et al .. Signalnoise ratio of total internal reflection edge illumination for optics damage inspection [J]. Infrared and laser Engineering , 2011, 40(6): 1111-1114. (in Chinese)

冯博, 刘炳国, 陈凤东, 等.ICF终端光学元件损伤在线检测装置的研究[J].红外与激光工程, 2013, 42(9):2519-2524.

FENG B, LIU B G, CHEN F D, et al .. Final optics damage online inspection system for ICF [J]. Infrared and laser Engineering , 2013, 42(9): 2519-2524. (in Chinese)

KEGELMEYER L M, FONG P W, GLENN S M, et al .. Local area signal-to-noise ratio (LASNR) algorithm for image segmentation [C]. Optics and Photonics for Information Processing , San Diego , CA , United states , 2007.

冯博.惯性约束聚变终端光学元件损伤在线检测技术研究[D].哈尔滨: 哈尔滨工业大学, 2014: 38-45. http://cdmd.cnki.com.cn/Article/CDMD-10213-1014084956.htm

FENG B. Research on Final Optics Damage Online Inspection Technologies for ICF System [D].Harbin: Harbin Institute of Technology, 2014: 38-45. (in Chinese)

CHEN C L P, LI H, WEI Y T, et al .. A local contrast method for small infrared target detection [J]. IEEE Trans on Geoscience and Remote Sensing , 2014, 52(1): 574-581.

王刚, 陈永光, 杨锁昌, 等.采用图像块对比特性的红外弱小目标检测[J].光学精密工程, 2015, 23(5): 1424-1432.

WANG G, CHEN Y G, YANG S CH, et al .. Detection of infrared dim small target based on image patch contrast [J]. Opt.Precision Eng ., 2015, 23(5): 1424-1432. (in Chinese)

田玉婷, 邬融, 杨野.基于局域信号增强的光学元件损伤检测[J].中国激光, 2018, 45(11):1104001.

TIAN Y T, WU R, YANG Y. Optical damage inspection based on local signal enhancement [J]. Chinese Journal of Lasers , 2018, 45(11):1104001. (in Chinese)

闫敬文, 陈宏达, 刘蕾.高光谱图像分类的研究进展[J].光学精密工程, 2019, 27(3): 680-693.

YAN J W, CHEN H D, LIU L. Overview of hyperspectral image classification [J]. Opt.Precision Eng ., 2019, 27(3): 680-693.(in Chinese)

张兵, 高连如.高光谱图像分类与目标探测[M].北京:科学出版社, 2011.

ZHANG B, GAO L R. High Spectral Image Classification and Target Detection [M]. Beijing:Science Press, 2011.(in Chinese)

张刘, 张皓晨, 刘付成, 等.基于高信背比的视频低速暗弱目标增强[J].光学精密工程, 2019, 27(4): 945-952.

ZHANG L, ZHANG H CH, LIU F CH, et al .. Video enhancement method for low-speed dim targets based on high signal-to-background ratio [J]. Opt.Precision Eng ., 2019, 27(4): 945-952.(in Chinese)

时春霖, 张超, 陈长远, 等.测量机器人小视窗星图一维最大熵星点图像分割算法[J].测绘学报, 2018, 47(4): 446-454.

SHI CH L, ZHANG CH, CHEN CH Y, et al .. One-dimensional maximum entropy image segmentation algorithm based on the small field of view of measuring robot star map [J] . Acta Geodaetica et Cartographica Sinica , 2018, 47(4), 446-454. (in Chinese)

AGARWAL A, ISSAC A, DUTTA M K. A region growing based imaging method for lesion segmentation from dermoscopic images [C]. IEEE Uttar Pradesh Section International Conference on Electrical , Computer and Electronics ( UPCON ), 2017: 632-637.

罗茂, 步杨, 徐静浩, 等.基于多光谱技术的光学元件表面此病检测[J].中国激光, 2017, 44(1): 0104001.

LUO M, BU Y, XU J H, et al ..Optical element surface defect measurement based on multispectral technique [J]. Chinese Journal of Lasers , 2017, 44(1):0104001. (in Chinese)

王淑青, 姚伟, 陈进, 等.基于直方图均衡化与形态学处理的边缘检测[J].计算机应用与软件, 2016, 33(3): 193-196.

WANG SH Q, YAO W, CHEN J, et al .. Image edge detection based on histogram equalisation and morphology processing [J]. Computer Applications and Software , 2016, 33(3): 193-196. (in Chinese)

Views

134

下载量

CSCD

Alert me when the article has been cited

提交

Tools

Publicity Resources

Adaptive color compensation and multi-scale fusion for underwater image enhancement

Degradation remote sensing image quality enhancement based on frequency-domain-spatial-domain hybrid attention

Underwater image enhancement using joint texture perception and color histogram features

Cross-level feature aggregation image enhancement with dual-path hybrid attention

Low-light image enhancement based on the fusion of Bilateral filter MSR and AutoMSRCR

Related Author

LIU Cuiyin

JING Tengyun

WANG Feng

CHEN Yuanshuai

WEI Hua

TANG Xiongxin

NIE Haitao

WANG Jing

Related Institution

School of Physics and Material Sciences， Guangzhou University

School of Information Engineering and Automation， Kunming University of Science and Technology

Changchun Institute of Optics，Fine Mechanics and Physics，Chinese Academy of Sciences

Laboratory of Science and Technology on Integrated Information System， Institute of Software， Chinese Academy of Sciences

Department of emergency management， Institute of Disaster Prevention

AI问答

Address：No.3888 Dong Nanhu Road, Changchun, Jilin, China Postal code：130033
Tel：0431-86176855 Email：gxjmgc@ciomp.ac.cn
Technical support is provided by Beijing Founder electronics co., LTD 吉ICP备11002662号-17 京公网安备11010802024621
It is recommended to read the content of this site in Chrome&IE9+. Please switch to extreme mode in browser 360.
Cookies We use cookies to help provide and enhance our service and tailor content. By continuing, you agree to the use of cookies.

⁰