The present invention relates to a near-field metasurface antenna fast computational imaging method based on a deep
convolutional neural network, including: reflecting the mapping relationship between echo measurement values and scene target
scattering coefficient distribution by constructing a measurement matrix; constructing a near-field MSACI-Net imaging network based on a
convolutional neural network, training the near-field MSACI-Net imaging network, and reconstructing a receiving matrix; designing a network training
algorithm according to the near-field MSACI-Net imaging network, using a
mean square error to calculate the loss between the network prediction value and the true value, and using the Adam optimization
algorithm to minimize the loss of the near-field MSACI-Net imaging network; using measured
field data to
train the near-field MSACI-Net imaging network, and reconstructing high-quality original target information in real time and with high precision after the training is completed. The present invention is based on a deep
convolutional neural network, and the constructed metasurface antenna computational imaging network quickly realizes near-field
metamaterial aperture calculation, and can quickly and accurately image point-sized targets and more complex targets.