Single pixel imaging is an innovative imaging scheme using active light to obtain spatial information, which has attracted much attention in the computational imaging field. However, for single pixel imaging, it is a great challenge to find an efficient technique to obtain imaging results with high quality under low sampling conditions. In order to solve this problem, a Fourier single pixel imaging (FSPI) based on a generative adversarial network (GAN) is proposed in this paper. In the proposed GAN model, perceptual loss, pixel and frequency loss are incorporated into the total loss function to better preserve the details of the target. With the help of the GAN model, the FSPI can reconstruct results with high quality at low sampling rate conditions. The numerical simulation and experiment are implemented. Compared with conventional FSPI and FSPI based on a deep convolutional auto-encoder network, the proposed method has a better visual effect and image quality evaluation index. This approach is particularly important to high speed single pixel imaging applications due to its potential for reconstructing the high-quality target image with a low sampling rate.

单像素成像是一种使用主动光获取空间信息的创新成像方案，在计算成像领域已引起广泛关注。然而，对于单像素成像，找到一种有效的技术以在低采样条件下获得高质量的成像结果是一个巨大的挑战。为了解决这个问题，本文提出了一种基于生成对抗网络（GAN）的傅立叶单像素成像（FSPI）。在提出的GAN模型中，将感知损失，像素损失和频率损失合并到总损失函数中，以更好地保留目标的细节。借助GAN模型，FSPI可以在低采样率条件下以高质量重建结果。进行了数值模拟和实验。与传统的基于深度卷积自动编码器网络的FSPI和FSPI相比，该方法具有更好的视觉效果和图像质量评价指标。这种方法对高速单像素成像应用特别重要，因为它具有以低采样率重建高质量目标图像的潜力。