Single-Pixel Imaging Reconstruction Network with Hybrid Attention and Enhanced U-Net - Bingrui Xiao; Huibin Wang; Yang Bu (Photonics, 2025)
Single-pixel imaging has the characteristics of a simple structure and low cost, which means it has potential applications in many fields. This paper proposes an image reconstruction method for single-pixel imaging (SPI) based on deep learning. This method takes the Generative Adversarial Network (GAN) as the basic architecture, combines the dense residual structure and the deep separable attention mechanism, and reduces the parameters while ensuring the diversity of feature extraction. It also reduces the amount of computation and improves the computational efficiency. In addition, dual-skip connections between the encoder and decoder parts are used to combine the original detailed information with the overall information processed by the network structure. This approach enables a more comprehensive and efficient reconstruction of the target image. Both simulations and experiments have confirmed that the proposed method can effectively reconstruct images at low sampling rates and also achieve good reconstruction results on natural images not seen during training, demonstrating a strong generalization capability.