Advanced Search
中文
Volume 45 Issue 4
Apr.  2023
Turn off MathJax
Article Contents
Abstract
References
Related Articles
LIAN Cheng, ZHANG Baohui, JIANG Yunfeng, JIANG Zhifang, ZHANG Qian, YUAN Xilin. An Infrared Image Enhancement Method Based on Semantic Segmentation[J]. Infrared Technology , 2023, 45(4): 394-401.
Citation: LIAN Cheng, ZHANG Baohui, JIANG Yunfeng, JIANG Zhifang, ZHANG Qian, YUAN Xilin. An Infrared Image Enhancement Method Based on Semantic Segmentation[J]. Infrared Technology , 2023, 45(4): 394-401.
shu

An Infrared Image Enhancement Method Based on Semantic Segmentation

  • 1.

    Kunming Institute of Physics, Kunming 650223, China

  • 2.

    North Night-Vision Science & Technology (Nanjing) Research Institute Co. LTD., Nanjing 211106, China

More Information
  • Received Date: November 16, 2022
  • Revised Date: December 10, 2022
    Graphical Abstract
    • Abstract
  • To solve the problem of visual unnaturalness caused by contrast-limited adaptive histogram equalization (CLAHE) forced blocking, this study proposes an infrared image enhancement method based on semantic segmentation. The semantic segmentation network segments the entire infrared image into category blocks instead of traditional rectangular image blocks. Each category block is individually subjected to contrast-limited histogram equalization to reduce over-enhancement. Finally, a new edge transition method is introduced to avoid abruptness between category blocks. The experimental results show that the proposed image enhancement method outperforms other contrast algorithms in terms of contrast and entropy and avoids the visual unnaturalness of traditional CLAHE with better visual effects.
    • FullText(HTML)
    • References (24)
  • [1]
    石川凌. 海陆场景的红外实时仿真研究[D]. 杭州: 浙江大学, 2016.

    SHI Chuanling. Study on Infrared Real-time Simulation of Sea-land Scene[D]. Hangzhou: Zhejiang University, 2016.
    [2]
    曾庆杰. 红外成像中图像质量提升算法研究[D]. 西安: 西安电子科技大学, 2021.

    ZENG Qingjie. Research on Image Quality Improvement Algorithm in Infrared Imaging[D]. Xi 'an: Xidian University, 2021.
    [3]
    William K. Pratt. Introduction to Digital Image Processing[M]. Taylor and Francis: CRC Press, 2013.
    [4]
    Jain A. Fundamentals of digital image processing[J]. Computer Vision, Graphics, and Image Processing, 1989, 46(3): 400-400.
    [5]
    Yeong-Taeg Kim. Contrast enhancement using brightness preserving bi-histogram equalization[J]. IEEE Transactions on Consumer Electronics, 1997, 43(1): 1-8. DOI: 10.1109/30.580378
    [6]
    Stark J A. Adaptive image contrast enhancement using generalizations of histogram equalization[J]. IEEE Transactions on Image Processing, 2000, 9(5): 889-96. DOI: 10.1109/83.841534
    [7]
    YANG Maoxiang, TANG Guijin, LIU Xiaohua, et al. Low-light image enhancement based on Retinex theory and dual-tree complex wavelet transform[J]. Optoelectronics Letters, 2018, 14(6): 470-475. DOI: 10.1007/s11801-018-8046-5
    [8]
    Zuiderveld K. Contrast limited adaptive histogram equalization[J]. Graphics Gems, 1994: 474-485.
    [9]
    Suharyanto, Hasibuan Z A, Andono P N, et al. Contrast limited adaptive histogram equalization for underwater image matching optimization use SURF[J]. Journal of Physics, 2021, 1803(1): 012008.
    [10]
    Ali M Reza. Realization of the contrast limited adaptive histogram equalization(CLAHE) for real-time image enhancement[J]. The Journal of VLSI Signal Processing, 2004, 38(1): 35-44. DOI: 10.1023/B:VLSI.0000028532.53893.82
    [11]
    Arici Tarik, Dikbas Salih, Altunbasak Yucel. A histogram modification framework and its application for image contrast enhancement[J]. IEEE Transactions on Image Processing, 2009, 18(9): 1921-1935. DOI: 10.1109/TIP.2009.2021548
    [12]
    Kim Wonkyun, You Jongmin, Jeong Jechang. Contrast enhancement using histogram equalization based on logarithmic mapping[J]. Optical Engineering, 2012, 51(6): 067002-1-067002-10. DOI: 10.1117/1.OE.51.6.067002
    [13]
    WU T, TANG S, ZHANG R, et al. CGNet: a light-weight context guided network for semantic segmentation[J]. IEEE Transactions on Image Processing, 2021, 30: 1169-1179. DOI: 10.1109/TIP.2020.3042065
    [14]
    Badrinarayanan Vijay, Kendall Alex, Cipolla Roberto. SegNet: a deep convolutional encoder-decoder architecture for image segmentation[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2017, 39(12): 2481-2495. DOI: 10.1109/TPAMI.2016.2644615
    [15]
    CHEN Soong-Der, Ramli Abd Rahman. Contrast enhancement using recursive mean-separate histogram equalization for scalable brightness preservation[J]. IEEE Trans. Consumer Electronics, 2003, 49(4): 1301-1309. DOI: 10.1109/TCE.2003.1261233
    [16]
    刘程威. 红外成像系统架构及图像处理关键技术研究[D]. 南京: 南京理工大学, 2020.

    LIU Chengwei. Research on Infrared Imaging System Architecture and Image Processing Key Technologies[D]. Nanjing: Nanjing University of Science and Technology, 2020.
    [17]
    Pizer S M, Amburn E P, Austin J D, et al. Adaptive histogram equalization and its variations[J]. Computer Vision, Graphics, and Image Processing, 1987, 39(3): 355-368. DOI: 10.1016/S0734-189X(87)80186-X
    [18]
    LI C, XIA W, YAN Y, et al. Segmenting objects in day and night: edge-conditioned CNN for thermal image semantic segmentation[J]. IEEE Transactions on Neural Networks and Learning Systems, 2020, 32(7): 3069-3082.
    [19]
    Silva Eric A, Panetta Karen, Agaian Sos S. Quantifying image similarity using measure of enhancement by entropy[J]. Mobile Multimedia/Image Processing for Military and Security Applications, 2007, 6579: 65790U-65790U-12. DOI: 10.1117/12.720087
    [20]
    ZHANG Yujin. Handbook of Image Engineering[M]. Singapore: Springer, 2021.
    [21]
    GAO Ce, YUN Lijun, WANG Kun, et al. Infrared image enhancement method based on discrete stationary wavelet transform and CLAHE[C]//IEEE International Conference on Computer Science and Educational Informatization(IEEE CSEI 2019), 2019: 52: DOI: 10.1109/CSEI47661.2019.8938871.
    [22]
    Gonzalez R C, Woods R E. Digital Image Processing[M]. Singapore: Pearson Prentice Hall, 2002: 75-215.
    [23]
    KIM Yeong-Taeg. Contrast enhancement using brightness preserving bi-histogram equalization[J]. IEEE Transactions on Consumer Electronics, 1997, 43(1): 1-8. DOI: 10.1109/30.580378
    [24]
    Turgay Celik. Two-dimensional histogram equalization and contrast enhancement[J]. Pattern Recognition, 2012, 45(10): 3810-3824. DOI: 10.1016/j.patcog.2012.03.019
    • Related Articles

  • Related Articles

    [1]LI Yaqing, YANG Zhuang, GAO Tianli, ZHOU Shengtao, LI Xiaolu, BAO Yuanxi, DU Peide, DAI Jinghao, HE Jun, ZHANG Liyun, SONG Qigeng, WANG Guangfan, XU Lingji, ZHANG Xu. Influence of Auto-Gated Power Supply on the Performance of Image Intensifier[J]. Infrared Technology , 2025, 47(4): 421-428.
    [2]NIU Qun, SHI Lixia, WANG Jinsong, TANG Zhuo. Low-light Image Enhancement Based on Detail Preservation and Brightness Fusion[J]. Infrared Technology , 2024, 46(10): 1162-1171.
    [3]LI Yaqing, ZHOU Shengtao, WANG Guangfan, CHU Zhujun, DU Peide, ZHU Wenjin, LI Xiaolu, ZUO Jianing, ZHU Shicong. Research on Brightness Gain Temperature Characteristics of Super Gen. II Low-Light-Level Image Intensifier Using High-voltage DC Power Supply[J]. Infrared Technology , 2022, 44(8): 804-810.
    [4]SU Yue, BAI Xiaofeng, DANG Xiaogang, FENG Danqing, CHENG Hongchang, LI Zhoukui, HAN Kun. Influence of Brightness Gain on the Object-Background Contrast of an Image Intensifier[J]. Infrared Technology , 2022, 44(4): 383-388.
    [5]YANG Ye, NI Xiaobing, YAN Bo, ZHI Qiang, LI Junguo. Study on the Relationship between Image Intensifier Cathode Pulse and Plate Brightness Stability[J]. Infrared Technology , 2018, 40(7): 691-694.
    [6]YAN Bo, YANG Ye, NI Xiaobing, ZHI Qiang, LI Junguo, DENG Guangxu. Relationship Between Cathode Pulse Duty Cycle and Phosphor Screen Current[J]. Infrared Technology , 2017, 39(8): 757-760.
    [7]MA Wenlong, QIU Yafeng. Research on Integrating Sphere Light Hole Brightness Attenuation Test System[J]. Infrared Technology , 2017, 39(4): 317-322.
    [8]NI Xiaobing, YAN Bo, YANG Ye, YANG Shuning, ZHI Qiang, LI Junguo, YAO Ze, DENG Guangxu. Study of Image Intensifier SNR Based on Auto Gated Power Supply[J]. Infrared Technology , 2017, 39(3): 284-287.
    [9]Study of Image Intensifier Dynamic Range Based on Auto-gating Power Source[J]. Infrared Technology , 2013, (5): 300-303.
    [10]HONG Ming, YI Ming, XIANG Zhen, WANG Xiao. Discuss on Experiment Methods of Protection Threshold of Highlight to Low-light Level Night-vision Device[J]. Infrared Technology , 2006, 28(2): 101-104. DOI: 10.3969/j.issn.1001-8891.2006.02.011

  • Cited by

    Periodical cited type(3)

    1. 张绘敏，赵扬，康会峰. 基于卷积神经网络算法的光伏组件热斑图像检测方法研究. 计算机测量与控制. 2024(07): 57-63 .
    2. 杨俊，高昱峰，张可，汪银，张雅琳，夏娜，姚钢. 基于图像边缘检测和法向探测的导线覆冰监测方法. 电网与清洁能源. 2023(02): 24-32 .
    3. 穆莉莉，汪晨灿，储汇，宋陈. 基于机器视觉的位移检测算法. 洛阳理工学院学报(自然科学版). 2022(04): 64-70 .

    Other cited types(2)

Catalog

    Get Citation
    PDF
    XML
    Article views PDF downloads Cited by(5)
    Related

    Copyright © 《Infrared Technology》Editorial Office滇ICP备12000354号-3

    Supported by: Beijing Renhe Information Technology Co., Ltd.  Baidu Analytics

    /

    DownLoad:  Full-Size Img  PowerPoint
    Return
    Return