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一种基于蚁狮最大熵算法与引导滤波的图像融合算法

蒋杰伟 刘尚辉 金库 魏戌盟 巩稼民

蒋杰伟, 刘尚辉, 金库, 魏戌盟, 巩稼民. 一种基于蚁狮最大熵算法与引导滤波的图像融合算法[J]. 电子与信息学报. doi: 10.11999/JEIT221499
引用本文: 蒋杰伟, 刘尚辉, 金库, 魏戌盟, 巩稼民. 一种基于蚁狮最大熵算法与引导滤波的图像融合算法[J]. 电子与信息学报. doi: 10.11999/JEIT221499
JIANG Jiewei, LIU Shanghui, JIN Ku, WEI Xumeng, GONG Jiamin. An Image Fusion Algorithm Based on Ant Lion Optimized Maximum Entropy Segmentation and Guided Filtering[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT221499
Citation: JIANG Jiewei, LIU Shanghui, JIN Ku, WEI Xumeng, GONG Jiamin. An Image Fusion Algorithm Based on Ant Lion Optimized Maximum Entropy Segmentation and Guided Filtering[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT221499

一种基于蚁狮最大熵算法与引导滤波的图像融合算法

doi: 10.11999/JEIT221499
基金项目: 国家自然科学基金(61775180, 62276210),陕西省自然科学基础研究计划(2022JM-380)
详细信息
    作者简介:

    蒋杰伟:男,博士,讲师,研究方向为人工智能、图像处理等

    刘尚辉:男,硕士生,研究方向为图像处理、图像融合等

    金库:男,硕士生,研究方向为光通信与光信息技术

    魏戌盟:女,硕士生,研究方向为光通信与光信息技术

    巩稼民:男,博士,教授,研究方向为光通信、光电子技术

    通讯作者:

    刘尚辉 lsh81687039@163.com

  • 中图分类号: TN713; TP391; TP18

An Image Fusion Algorithm Based on Ant Lion Optimized Maximum Entropy Segmentation and Guided Filtering

Funds: The National Natural Science Foundation of China (61775180, 62276210), The Natural Science Basic Research Program of Shaanxi (2022JM-380)
  • 摘要: 传统红外与可见光图像融合算法中易出现目标提取不够充分、细节丢失等问题,导致融合效果不理想,从而无法应用于目标检测、跟踪或识别等领域。因此,该文提出一种基于蚁狮优化算法(ALO)改进的最大香农(Shannon)熵分割法结合引导滤波的红外与可见光图像融合方法。首先,使用蚁狮最大熵分割法(ALO-MES)对红外图像进行目标提取,然后,对红外和可见光图像使用非下采样剪切波变换(NSST),并对获得的低频和高频分量进行引导滤波。由提取的目标图像与增强后的红外和可见光低频分量通过低频融合规则得到低频融合系数,增强后的高频分量通过双通道脉冲发放皮层模型(DCSCM)得到高频融合系数,最后经NSST逆变换得到融合图像。实验结果表明,所提算法能够得到目标明确、背景信息清晰的融合图像。
  • 图  1  本文融合方法流程图

    图  2  3种不同图像分割方法效果对比

    图  3  5组可见光与红外光图像

    图  4  不同算法融合结果对比

    表  1  4种图像分割算法对比

    算法Iter(1)/ Th(1)Iter(2)/ Th(2)Iter(3)/ Th(3)AV-IterAV-Time(ms)
    MES1/1551/1551/1551.0134
    PSO-MES2/1542/1572/1682.093
    FA-MES2/1543/1553/1552.6109
    ALO-MES2/1551/1552/1551.643
    下载: 导出CSV

    表  2  5组融合图像客观评价指标

    实验图像组算法AGSTDMIENQAB/FSSIM
    manMGFF5.776532.00721.50416.80100.42010.5480
    RGFFT5.665139.31423.00867.16540.47810.5105
    MST5.507633.13312.83166.65620.44020.5192
    SCMGF5.496339.41703.42527.16430.47720.5186
    本文5.794339.57542.68107.16870.43360.5314
    meetingMGFF5.688631.54101.42706.86070.47590.1299
    RGFFT5.251745.97612.35986.95190.45460.4904
    MST5.338954.60094.22856.79750.55620.4873
    SCMGF5.638436.47191.83047.17680.43750.4795
    本文5.458646.17902.46606.95860.47700.4978
    trafficMGFF4.470731.89441.88726.53960.56530.4394
    RGFFT4.502735.82594.37996.76870.67210.4747
    MST4.141329.75692.62956.25250.55750.4219
    SCMGF4.200435.12442.78516.77250.51750.4054
    本文4.592737.52403.43776.77930.60810.4712
    treeMGFF7.736426.44330.95840.95840.37060.5471
    RGFFT7.060140.44184.19327.16270.55100.5181
    MST7.267535.55882.69916.77200.44490.5409
    SCMGF6.889140.15623.26757.15760.47720.5272
    本文7.490540.49022.86347.16640.42790.5537
    KapteinMGFF6.026735.46981.52546.68350.47070.5218
    RGFFT5.917156.91603.41777.27800.51150.4509
    MST5.841247.72833.27526.60780.50630.4955
    SCMGF5.724458.60634.57817.27610.52730.4796
    本文6.199361.22493.46647.39850.48880.4888
    下载: 导出CSV
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出版历程
  • 收稿日期:  2022-12-02
  • 修回日期:  2022-12-23
  • 网络出版日期:  2022-12-28

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