Motion Defocus Infrared Image Restoration Based on Multi Scale Generative Adversarial Network
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摘要:
红外热成像系统在夜间实施目标识别与检测优势明显，而移动平台上动态环境所导致的运动散焦模糊影响上述成像系统的应用。该文针对上述问题，基于生成对抗网络开展运动散焦后红外图像复原方法研究，采用生成对抗网络抑制红外图像的运动散焦模糊，提出一种针对红外图像的多尺度生成对抗网络(IMdeblurGAN)在高效抑制红外图像运动散焦模糊的同时保持红外图像细节对比度，提升移动平台上夜间目标的检测与识别能力。实验结果表明：该方法相对已有最优模糊图像复原方法，图像峰值信噪比(PSNR)提升5%，图像结构相似性(SSIMx)提升4%，目标识别YOLO置信度评分提升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%.
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表 1 复原性能对比分析
复原方法 平均峰值信噪比 (dB) 平均结构相似性 Wiener 21.3 0.62 LR 22.5 0.65 DeblurGAN 27.0 0.75 SRN-DeblurNet 30.5 0.88 本文IMdeblurGAN 32.0 0.92 
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表 2 YOLO V3置信度对比分析
原始图像 运动散焦图像 Wiener逆滤波 LR迭代去卷积 DeblurGAN SRN-DeblurNet 本文IMdeblurGAN YOLOV3 评分 0.97 不能识别 不能识别 不能识别 0.77 0.89 0.95
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