ZHANG Huan, CHEN Zhisheng. Multi-scale Auto-Corrected Bi-Histogram Equalization for Infrared Image Enhancement[J]. Infrared Technology , 2023, 45(11): 1207-1215.
Citation: ZHANG Huan, CHEN Zhisheng. Multi-scale Auto-Corrected Bi-Histogram Equalization for Infrared Image Enhancement[J]. Infrared Technology , 2023, 45(11): 1207-1215.

Multi-scale Auto-Corrected Bi-Histogram Equalization for Infrared Image Enhancement

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  • Received Date: February 05, 2023
  • Revised Date: March 30, 2023
  • We proposed a parameter self-tuning bi-histogram equalization method to solve saturation and detail loss in infrared image enhancement. We decomposed an input image into two independent sub-images according to the golden ratio of the gray cumulative probability density and modified each sub-image histogram through a multi-scale adaptive weighing process with input image exposure and sub-image gray-level interval information. Subsequently, we performed the equalization of the two corrected sub-histograms independently and combined the two equalized sub-images into a single output image. A test on 100 infrared images in a public dataset-INFRARED100 showed that, compared with brightness preserving bi-histogram equalization (BBHE), bi-histogram equalization with a plateau limit (BHEPL), and exposure-based sub-image histogram equalization (ESIHE), the images enhanced by the proposed method have appropriate contrast and greater average information entropy. We increased the peak signal-to-noise ratio (PSNR), structural similarity (SSIM) index, and absolute mean brightness error (AMBE) by at least 17.2%, 4.0%, and 56.2% on average. The experiments illustrated that the proposed method is adaptable to infrared images with different brightness characteristics, effectively improving the contrast between the infrared image object and background. This method is superior to noise suppression, brightness, and detail preservation methods.
  • [1]
    孔松涛, 黄镇, 杨谨如. 红外热像无损检测图像处理研究现状与进展[J]. 红外技术, 2019, 41(12): 1133-1140. http://hwjs.nvir.cn/article/id/hwjs201912007

    KONG S, HUANG Z, YANG J. Research status and development of image processing for infrared thermal image nondestructive testing[J]. Infrared Technology, 2019, 41(12): 1133-1140. http://hwjs.nvir.cn/article/id/hwjs201912007
    [2]
    ZUO J, HU X, XU L, et al. CH4 gas leakage detection method for low contrast infrared images [J]. Infrared Physics & Technology, 2022, 127: 104473.
    [3]
    HE Z, TANG S, YANG J, et al. Cascaded deep networks with multiple receptive fields for infrared image super-resolution[J]. IEEE Transactions on Circuits and Systems for Video Technology, 2019, 29(8): 2310-2322. DOI: 10.1109/TCSVT.2018.2864777
    [4]
    RASHEED M T, SHI D, KHAN H. A comprehensive experiment-based review of low-light image enhancement methods and benchmarking low-light image quality assessment[J]. Signal Processing, 2023, 204: 108821 DOI: 10.1016/j.sigpro.2022.108821
    [5]
    GONZALEZ R, WOODS R. Digital Image Processing[M]. 4th edition, New York: Pearson, 2018.
    [6]
    HUANG S C, CHENG F C, CHIU Y S. Efficient contrast enhancement using adaptive Gamma correction with weighting distribution[J]. IEEE Transactions on Image Processing, 2013, 22(3): 1032-1041. DOI: 10.1109/TIP.2012.2226047
    [7]
    胡家珲, 詹伟达, 桂婷婷, 等. 基于多尺度加权引导滤波的红外图像增强方法[J]. 红外技术, 2022, 44(10): 1082-1088. http://hwjs.nvir.cn/article/id/be19ce07-80c0-43ee-85ef-7f82fc8988d9

    HU J, ZHAN W, GUI T, et al. Infrared image enhancement method based on multiscale weighted guided filtering [J]. Infrared Technology, 2022, 44(10): 1082-1088. http://hwjs.nvir.cn/article/id/be19ce07-80c0-43ee-85ef-7f82fc8988d9
    [8]
    KIM Y T. Contrast enhancement using brightness preserving bi-histogram equalization[J]. IEEE Transactions on Consumer Electronics, 1997, 43(1): 1-8. DOI: 10.1109/30.580378
    [9]
    RAHMAN H, PAUL G C. Tripartite sub-image histogram equalization for slightly low contrast gray-tone image enhancement[J]. Pattern Recognition, 2023, 134: 109043. DOI: 10.1016/j.patcog.2022.109043
    [10]
    PAUL A. Adaptive tri-plateau limit tri-histogram equalization algorithm for digital image enhancement[J]. Visual Computer, 2023, 39: 297-318. DOI: 10.1007/s00371-021-02330-z
    [11]
    Caballero R, Pineda I, Román J, et al. Quadri-histogram equalization for infrared images using cut-off limits based on the size of each histogram [J]. Infrared Physics & Technology, 2019, 99: 257-264.
    [12]
    Rao B S. Dynamic histogram equalization for contrast enhancement for digital images[J]. Applied Soft Computing, 2020, 89: 106114. DOI: 10.1016/j.asoc.2020.106114
    [13]
    江巨浪, 刘国明, 朱柱, 等. 基于快速模糊聚类的动态多直方图均衡化算法[J]. 电子学报, 2022, 50(1): 167-176. https://www.cnki.com.cn/Article/CJFDTOTAL-DZXU202201019.htm

    JIANG J, LIU G, ZHU Z, et al. Dynamic multi-histogram equalization based on fast fuzzy clustering[J]. Acta Electronica Sinica, 2022, 50(1): 167-176. https://www.cnki.com.cn/Article/CJFDTOTAL-DZXU202201019.htm
    [14]
    闫哲, 蒋砾, 杨帆, 等. 基于双直方图均衡算法的红外图像增强[J]. 红外技术, 2022, 44(9): 944-950. http://hwjs.nvir.cn/article/id/fd34432d-340f-429b-b2ae-790b2c85b4b0

    YAN Z, JIANG L, YANG F, et al. Bi-histogram equalization algorithm for infrared image enhancement [J]. Infrared Technology, 2022, 44(9): 944-950. http://hwjs.nvir.cn/article/id/fd34432d-340f-429b-b2ae-790b2c85b4b0
    [15]
    OOI C H, PIK K, IBRAHIM H. Bi-histogram equalization with a plateau limit for digital image enhancement [J]. IEEE Transactions on Consumer Electronics, 2009, 55(4): 2072-2080. DOI: 10.1109/TCE.2009.5373771
    [16]
    Bhandari A K, Kandhway P, Maurya S. Salp Swarm algorithm-based optimally weighted histogram framework for image enhancement[J]. IEEE Transactions on Instrumentation and Measurement, 2020, 69(9): 6807-6815. DOI: 10.1109/TIM.2020.2976279
    [17]
    Kandhway P, Bhandari A K, Singh A. A novel reformed histogram equalization based medical image contrast enhancement using krill herd optimization [J]. Biomedical Signal Processing and Control, 2020, 56: 101677.
    [18]
    Majeed S H, Isa N A M. Iterated adaptive entropy-clip limit histogram equalization for poor contrast images[J]. IEEE Access, 2020, 8: 144218-144245.
    [19]
    Singh K, Kapoor R. Image enhancement using exposure based sub image histogram equalization [J]. Pattern Recognition Letters, 2014, 36: 10-14.
    [20]
    谢凤英. 数字图像处理及应用[M]. 第2版, 北京: 电子工业出版社, 2016.

    XIE F. Digital Image Processing and Application[M]. 2nd edition Beijing: Publishing House of Electronics Industry, 2016.
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