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采用卷积神经网络提高鬼成像的边缘质量

张航宇 吴仪 赵帅 冯国英

张航宇, 吴仪, 赵帅, 等. 采用卷积神经网络提高鬼成像的边缘质量[J]. 强激光与粒子束, 2024, 36: 079002. doi: 10.11884/HPLPB202436.240030
引用本文: 张航宇, 吴仪, 赵帅, 等. 采用卷积神经网络提高鬼成像的边缘质量[J]. 强激光与粒子束, 2024, 36: 079002. doi: 10.11884/HPLPB202436.240030
Zhang Hangyu, Wu Yi, Zhao Shuai, et al. Edge quality improvement of ghost imaging based on convolutional neural network[J]. High Power Laser and Particle Beams, 2024, 36: 079002. doi: 10.11884/HPLPB202436.240030
Citation: Zhang Hangyu, Wu Yi, Zhao Shuai, et al. Edge quality improvement of ghost imaging based on convolutional neural network[J]. High Power Laser and Particle Beams, 2024, 36: 079002. doi: 10.11884/HPLPB202436.240030

采用卷积神经网络提高鬼成像的边缘质量

doi: 10.11884/HPLPB202436.240030
基金项目: 国家重点研发计划项目 (2022YFB3606304);国家自然科学基金项目 (U2230129)
详细信息
    作者简介:

    张航宇,zhanghangyu@stu.scu.edu.cn

    通讯作者:

    冯国英,guoing_feng@scu.edu.cn

  • 中图分类号: O438

Edge quality improvement of ghost imaging based on convolutional neural network

  • 摘要: 散斑移位鬼成像边缘提取方法需要对物体进行多次的采样,才能得到高质量的边缘图。为了解决散斑移位鬼成像提取物体边缘时采样次数多和时间长的问题,在鬼成像的边缘提取实验中采用了卷积神经网络。首先用Walsh散斑对未知图像进行照射,将桶探测器收集的采样信号作为图像特征信息输入到鬼成像边缘提取网络,最后通过训练好的网络直接输出探测物体的边缘信息图,并且使用非极大值抑制算法来优化卷积神经网络的输出结果。实验结果表明,对于128×128像素的重建物体,在采样次数为1600时,鬼成像边缘提取网络输出边缘图案的信噪比和结构相似指数分别比散斑移位鬼成像的输出结果提高了5倍和2倍,成功提高了低采样率下鬼成像边缘提取的质量,降低了采样的时间。采用卷积神经网络的鬼成像边缘提取方案,有利于鬼成像在物体识别、安全检查的实际应用中进行快速高质量的边缘检测。
  • 图  1  采用Unet卷积神经网络的鬼成像边缘提取方案示意图

    Figure  1.  Schematic diagram of the ghost imaging edge detection scheme using Unet convolutional neural network

    图  2  Unet鬼成像边缘提取网络(conv 3×3: 尺寸为3×3大小的卷积核;relu: 激活函数;BN: 批归一化层;maxpool 2×2: 尺寸为2×2大小的最大池化层;maxunpool 2×2: 尺寸为2×2大小的最大反池化层; skip connection: 跳跃连接,将编码信息与解码信息相加;sigmoid: 激活函数,将输入值映射到0~1的概率)

    Figure  2.  Unet ghost imaging edge detection network (conv 3×3: convolutional kernel of size 3×3; relu is the activation function; BN: batch normalization layer; maxpool 2×2: maximum pooling layer of size 2×2; maxunpool 2×2: maximum inverse pooling layer of size 2×2; skip connection: jump connection to sum the encoded information with decoded information; sigmoid: activation function, maps input values to 0−1 probabilities)

    图  3  数据集中的图片样例

    Figure  3.  Sample images from the dataset

    图  4  Unet鬼成像边缘提取网络的训练损失变化过程和学习率变化曲线

    Figure  4.  Training loss variation process and learning rate variation curve of Unet ghost imaging edge extraction network

    图  5  测试集的Unet鬼成像边缘提取网络输出边缘图和非极大值抑制算法处理后的边缘图

    Figure  5.  Output edge images of Unet ghost imaging edge detection network for the test set and the edge images processed by the non-maximal value suppression algorithm

    图  6  多个字母或数字对应的Unet鬼成像边缘提取网络输出图

    Figure  6.  Output images of Unet ghost imaging edge detection network corresponding to multiple letters or numbers

    图  7  字母K和数字7通过Unet鬼成像边缘提取网络输出的边缘结果与通过散斑移位鬼成像方法输出的边缘结果的对比

    Figure  7.  Comparison of the edge results output by the Unet ghost imaging edge detection network for the letter K and the number 7 with those output by the scatter-shift ghost imaging method

    图  8  采样次数不同时,字母K和数字7通过Unet鬼成像边缘提取网络输出的边缘结果与通过散斑移位鬼成像方法输出的边缘结果的SNR和SSIM指标对比

    Figure  8.  SNR and SSIM metrics of the edge results output by the Unet ghost imaging edge detection network compared to those output by the scatter-shift ghost imaging method as the number of samples increases for the letter K and the number 7

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出版历程
  • 收稿日期:  2024-01-22
  • 修回日期:  2024-05-09
  • 录用日期:  2024-05-09
  • 网络出版日期:  2024-05-22
  • 刊出日期:  2024-05-31

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