Single-Pixel Imaging Based on Enhanced Multi-Network Prior

Single-pixel imaging (SPI) is a significant branch of computational imaging. Owing to the high sensitivity, low cost, and wide spectrum, it acquires extensive applications across various domains. Nevertheless, multiple measurements and long reconstruction time constrain its application. The application of neural networks has significantly improved the quality of reconstruction, but there is still a huge space for improvement in performance. SAE and Unet have different advantages in the field of SPI. However, there is no method that combines the advantages of these two networks for SPI reconstruction. Therefore, we propose the EMNP-SPI method for SPI reconstruction using SAE and Unet networks. The SAE makes use of the measurement dimension information and uses the group inverse to obtain the decoding matrix to enhance its generalization. The Unet uses different size convolution kernels and attention mechanisms to enhance feature extraction capabilities. Simulations and experiments confirm that our proposed enhanced multi-network prior method can significantly improve the quality of image reconstruction at low measurement rates.