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红外图像动态范围压缩算法研究综述

欧阳慧明 李泽民 周永康 王世锦 朱晓杰 曾邦泽 赵德利 胡建钏

欧阳慧明, 李泽民, 周永康, 王世锦, 朱晓杰, 曾邦泽, 赵德利, 胡建钏. 红外图像动态范围压缩算法研究综述[J]. 红外技术, 2021, 43(3): 208-217.
引用本文: 欧阳慧明, 李泽民, 周永康, 王世锦, 朱晓杰, 曾邦泽, 赵德利, 胡建钏. 红外图像动态范围压缩算法研究综述[J]. 红外技术, 2021, 43(3): 208-217.
OUYANG Huiming, LI Zemin, ZHOU Yongkang, WANG Shijin, ZHU Xiaojie, ZENG Bangze, ZHAO Deli, HU Jianchuan. Review of Dynamic Range Compression Algorithms for Infrared Images[J]. INFRARED TECHNOLOGY, 2021, 43(3): 208-217.
Citation: OUYANG Huiming, LI Zemin, ZHOU Yongkang, WANG Shijin, ZHU Xiaojie, ZENG Bangze, ZHAO Deli, HU Jianchuan. Review of Dynamic Range Compression Algorithms for Infrared Images[J]. INFRARED TECHNOLOGY, 2021, 43(3): 208-217.

红外图像动态范围压缩算法研究综述

详细信息
    作者简介:

    欧阳慧明(1991-),男,白族,硕士,工程师,主要研究方向:红外成像系统设计及相关技术。E-mail: 799049681@qq.com

  • 中图分类号: TP751.1

Review of Dynamic Range Compression Algorithms for Infrared Images

  • 摘要: 红外图像的动态范围压缩是红外图像可视化研究领域的重要研究方向。红外图像的动态范围压缩算法将直接决定原始红外图像的细节保留、整体观感等重要可视化指标,某种意义上也可以说是细节增强的基础及保障。基于此,本文调研了当前主流的宽动态红外图像的动态范围压缩算法,将其分为基于全局压缩算法和基于局部压缩算法两大类,并对这两类算法的核心思想、发展过程及优缺点分别进行研究分析并提出了改进方向及发展趋势,为相关研究者提供参考。
  • 图  1  线性压缩和三段式线性压缩及最优化曲线压缩处理对比

    Figure  1.  Comparison between linear compression and three-stage linear compression and optimal curve compression processing

    图  2  指数压缩、三角函数压缩及S型曲线压缩处理结果对比

    Figure  2.  Comparison of index compression, trigonometric function compression and S-curve compression

    图  3  对数压缩及其两种改进的对数压缩方法处理后图片

    Figure  3.  Logarithmic compression and two improved methods of logarithmic compression after image processing

    图  4  γ函数压缩及其两种改进的自适应γ压缩方法处理后图片

    Figure  4.  Gamma compression and two improved adaptive gamma compression methods for post-processing images

    图  5  线性压缩、直方图均衡化与HALEQ算法处理效果对比

    Figure  5.  Comparison of linear compression, histogram equalization and HALEQ algorithm

    图  6  直方图修正算法处理结果

    Figure  6.  Histogram modification algorithm processing results

    图  7  局部直方图类算法处理结果对比

    Figure  7.  Comparison of processing results of local histogram algorithms

    表  1  红外图像动态范围压缩方法

    Table  1.   Infrared image dynamic range compression methods

    Classification Typical method Advantage Disadvantage
    Global compression method Linear compression AGC Segmented AGC The method is simple and the processing speed is fast Need to adjust the gain coefficient manually, the detail loss is obvious, the contrast is poor
    Nonlinear compression Exponential compression, S-curve compression Trigonometric function Compression, OMCP, ML & LC, BAC & LC LM & AC, BF & AGC The amplification of noise is suppressed, the relative change of brightness is retained, and the computational complexity is relatively low Without considering the local features of the image, the image details are obviously lost after processing, the local contrast is low, and the scene adaptability of some algorithms is poor
    Histogram class compression HE, PHE, HALEQ The method is simple, the processing speed is fast, and the image contrast is relatively good The algorithm is relatively limited and the image details are obviously lost after processing
    Local histogram compression method AHE, CLAHE, POAHE Clahe algorithm based on Gaussian weight, Combine global compression with local compression Considering the local features of the image, the details and local contrast of the processed image are greatly improved Because the partitioning effect is easy to introduce artifacts in the area of the image gray scale, the noise cannot be effectively suppressed, and the overall image contrast is relatively poor
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  • 收稿日期:  2020-07-30
  • 修回日期:  2020-10-14
  • 刊出日期:  2021-04-02

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