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作 者:苏迪 王少博 张成[1] 陈志升 刘超越 SU Di;WANG Shaobo;ZHANG Cheng;CHEN Zhisheng;LIU Chaoyue(School of Aerospace Engineering,Beijing Institute of Technology,Beijing 100081,China;National Institute of Extremely-Weak Magnetic Field Infrastructure,Hangzhou 310051,Zhejiang,China;China Academy of Launch Vehicle Technology,Beijing 100076,China)
机构地区:[1]北京理工大学宇航学院,北京100081 [2]杭州极弱磁场国家重大科技基础设施研究院,浙江杭州310051 [3]中国运载火箭技术研究院,北京100076
出 处:《兵工学报》2024年第3期855-863,共9页Acta Armamentarii
摘 要:低速旋转弹导引头具有抖动和旋转等运动特性,使得其采集图像存在严重的模糊特点,直接影响了后续图像算法对目标识别的准确性,进而影响制导精度。针对以上问题,提出一种基于生成对抗神经网络的盲去模糊算法。利用复合运动模糊仿真系统,实现了对弹载图像抖动和旋转等运动模糊的模拟,并制作弹载图像模糊数据集。使用卷积神经网络作为生成器和判别器的基本架构,并设计多个损失函数共同优化网络,以减小图像在修复复原过程中的噪声并保持图像平滑。实现了弹载图像的去模糊,并获得更加稳定、清晰的图像序列。实验结果表明,该算法的去模糊效果在峰值信噪比、结构相似性指标上比现阶段其他算法更好,更符合人类视觉的主观感受,具有工程应用价值。The low-speed roll missile seeker has a serious blurring feature in image collection due to its motion characteristics such as dithering and rotating,which directly affects the accuracy of subsequent image algorithms for target recognition,thus affecting the guidance accuracy.To solve the above problems,a blind deblurring algorithm based on a generative adversarial network is proposed.The motion blurring simulation system is used to simulate the motion blurring such as jitter and rotation of missile-borne image,and a fuzzy dataset of missile-borne image is made.The convolution neural network is used as the basic architecture of generator and discriminator,and several loss functions are designed to optimize the network together to reduce the noise and keep the image smooth during image restoration.The de-blurring of missile-borne image is achieved,and a more stable and clear image sequence is obtained.The experimental results show that the proposed algorithm performs better in peak signal-to-noise ratio and structural similarity than other algorithms and achieves state-of-the-art performance,and accords with the subjective perception of human vision.It has practical application value.
分 类 号:TJ765[兵器科学与技术—武器系统与运用工程]
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