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基于多尺度生成對抗網(wǎng)絡(luò)的運動散焦紅外圖像復(fù)原

易詩,  吳志娟,  朱競銘,  李欣榮,  袁學(xué)松

易詩, 吳志娟, 朱競銘, 李欣榮, 袁學(xué)松. 基于多尺度生成對抗網(wǎng)絡(luò)的運動散焦紅外圖像復(fù)原[J]. 電子與信息學(xué)報, 2020, 42(7): 1766-1773. doi: 10.11999/JEIT190495
引用本文: 易詩, 吳志娟, 朱競銘, 李欣榮, 袁學(xué)松. 基于多尺度生成對抗網(wǎng)絡(luò)的運動散焦紅外圖像復(fù)原[J]. 電子與信息學(xué)報, 2020, 42(7): 1766-1773. doi: 10.11999/JEIT190495
Shi YI, Zhijuan WU, Jingming ZHU, Xinrong LI, Xuesong YUAN. Motion Defocus Infrared Image Restoration Based on Multi Scale Generative Adversarial Network[J]. Journal of Electronics & Information Technology, 2020, 42(7): 1766-1773. doi: 10.11999/JEIT190495
Citation: Shi YI, Zhijuan WU, Jingming ZHU, Xinrong LI, Xuesong YUAN. Motion Defocus Infrared Image Restoration Based on Multi Scale Generative Adversarial Network[J]. Journal of Electronics & Information Technology, 2020, 42(7): 1766-1773. doi: 10.11999/JEIT190495

基于多尺度生成對抗網(wǎng)絡(luò)的運動散焦紅外圖像復(fù)原

doi: 10.11999/JEIT190495 cstr: 32379.14.JEIT190495
  • 1.

    成都理工大學(xué)信息科學(xué)與技術(shù)學(xué)院 成都 610051

  • 2.

    電子科技大學(xué)電子科學(xué)與工程學(xué)院 成都 610054

基金項目: 國家自然科學(xué)基金(61771096)
詳細(xì)信息
    作者簡介:

    易詩：男，1983年生，高級實驗師，研究方向為深度學(xué)習(xí)、紅外圖像處理

    袁學(xué)松：男，1980年生，教授，研究方向為太赫茲成像技術(shù)、信號與信息處理

    通訊作者:

    易詩　549745481@qq.com

  • 中圖分類號: TN911.73

Motion Defocus Infrared Image Restoration Based on Multi Scale Generative Adversarial Network

  • 1.

    College of Information Science and Technology, Chengdu Universitly of Technology, Chengdu 610051, China

  • 2.

    College of Electronic Science and Engineering, University of Electronic Science and Technology, Chengdu 610054, China

Funds: The National Natural Science Foundation of China (61771096)
  • 摘要:

    紅外熱成像系統(tǒng)在夜間實施目標(biāo)識別與檢測優(yōu)勢明顯，而移動平臺上動態(tài)環(huán)境所導(dǎo)致的運動散焦模糊影響上述成像系統(tǒng)的應(yīng)用。該文針對上述問題，基于生成對抗網(wǎng)絡(luò)開展運動散焦后紅外圖像復(fù)原方法研究，采用生成對抗網(wǎng)絡(luò)抑制紅外圖像的運動散焦模糊，提出一種針對紅外圖像的多尺度生成對抗網(wǎng)絡(luò)(IMdeblurGAN)在高效抑制紅外圖像運動散焦模糊的同時保持紅外圖像細(xì)節(jié)對比度，提升移動平臺上夜間目標(biāo)的檢測與識別能力。實驗結(jié)果表明：該方法相對已有最優(yōu)模糊圖像復(fù)原方法，圖像峰值信噪比(PSNR)提升5%，圖像結(jié)構(gòu)相似性(SSIMx)提升4%，目標(biāo)識別YOLO置信度評分提升6%。

    Abstract:

    Infrared thermal imaging system has obvious advantages in target recognition and detection at night, and the motion defocus blur caused by dynamic environment on mobile platform affects the application of the above imaging system. In order to solve the above problems, based on the research of infrared image restoration method after motion defocusing using generating confrontation network, a Infrared thermal image Multi scale deblurGenerative Adversarial Network (IMdeblurGAN) is proposed to suppress motion defocusing blurring effectively while preserving the image by using generating confrontation network to suppress the motion defocusing blurring of infrared image to hold the contrast of infrared image details, to improve the detection and recognition ability of night targets on motion platform. The experimental results show that compared with the existing optimal restoration methods for blurred images, Peak Signal to Noise Ratio (PSNR) of the image is increased by 5%, the Structure SIMilarity (SSIM) is increased by 4%, and the confidence score of YOLO for target recognition is increased by 6%.

    • HTML全文

  • 圖  1  散焦模糊軌跡

    圖  2  生成對抗網(wǎng)絡(luò)還原運動散焦紅外圖像

    圖  3  生成網(wǎng)絡(luò)結(jié)構(gòu)

    圖  4  判別網(wǎng)絡(luò)結(jié)構(gòu)

    圖  5  復(fù)原效果對比

    圖  6  復(fù)原細(xì)節(jié)對比測試

    圖  7  YOLOV3置信度對比

    表  1  復(fù)原性能對比分析

    復(fù)原方法平均峰值信噪比 (dB)平均結(jié)構(gòu)相似性
    Wiener21.30.62
    LR22.50.65
    DeblurGAN27.00.75
    SRN-DeblurNet30.50.88
    本文IMdeblurGAN32.00.92
    下載: 導(dǎo)出CSV

    表  2  YOLO V3置信度對比分析

    原始圖像運動散焦圖像Wiener逆濾波LR迭代去卷積DeblurGANSRN-DeblurNet本文IMdeblurGAN
    YOLOV3 評分0.97不能識別不能識別不能識別0.770.890.95
    下載: 導(dǎo)出CSV
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出版歷程
  • 收稿日期:  2019-07-03
  • 修回日期:  2020-01-22
  • 網(wǎng)絡(luò)出版日期:  2020-03-25
  • 刊出日期:  2020-07-23

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