Fourier single-pixel imaging (FSI) usually achieves an enhanced imaging speed via undersampling; however, many details are lost in the reconstructed image obtained by frequency truncation due to the lack of the high-frequency part, and the image contains ringing artefacts due to the lack of an expression ability, thus reducing the image quality. To improve the quality of real-time imaging, a fast image reconstruction framework based on the Wasserstein generative adversarial network (WGAN) and gradient penalty (GP) is proposed. Under the common constraints of the resistance loss and content loss, confrontation training is first conducted between the generator and the discriminator in the framework, and then, an additional generator is connected to improve the fidelity of the reconstructed image based on the generation of the confrontation network. The model can be used to restore high-frequency details and denoise low-quality images that are undersampled. The simulation and experimental results show that the FSI using the GAN method, which achieved a compression rate of 95–98 %, is superior to conventional imaging in terms of the quality at a low sampling rate; therefore, the proposed scheme has potential practical applications.

傅里叶单像素成像（FSI）通常通过欠采样来提高成像速度。然而，由于缺少高频部分，所以在通过频率截断获得的重建图像中丢失了许多细节，并且由于缺乏表达能力，图像中包含了振铃伪影，从而降低了图像质量。为了提高实时成像的质量，提出了一种基于Wasserstein生成对抗网络（WGAN）和梯度惩罚（GP）的快速图像重建框架。在阻力损失和内容损失的共同约束下，首先在框架中的生成器和鉴别器之间进行对抗训练，然后再连接一个附加的生成器，以基于对抗的产生来提高重建图像的保真度。网络。该模型可用于恢复高频细节，并去除欠采样的低质量图像。仿真和实验结果表明，采用GAN方法的FSI的压缩率达到95-98％，在低采样率的质量上优于常规成像。因此，该方案具有潜在的实际应用价值。