To address the problems of poor robustness and high computational complexity of some existing coded aperture imaging algorithms, an enhanced terahertz coded aperture imaging method based on convolutional neural networks was proposed in this paper. The method realized implicit modeling of the imaging system by constructing an end-to-end neural network that exploited the network′s strong inversion ability and noise immunity for target reconstruction at low SNRs (signal-to-noise ratios). The simulation experiments show that the proposed method can achieve the reconstruction of targets with different sparseness at different SNRs. Moreover, the proposed method can achieve a higher resolution reconstruction of the target at low SNRs in comparison with to the classical optimization iterative algorithm.