Abstract: Single pixel imaging is an imaging technique that reconstructs a complete image using only nonresolving bucket detectors combined with spatial light modulation information. It features nonlocal imaging and high sensitivity, making it suitable for ultra long distance imaging and detection of noncooperative targets in outer space. However, it requires multiple spatial light modulations for detection, resulting in low signal to noise ratio in the reconstructed images. A global attention mechanism based image enhancement method for low sampling rates is presented in this paper. A novel SUNet(swin transformer unet)network is built via Transformer architecture to address the issues of translational invariance and limited global receptive field in traditional convolutional neural networks. Improved differential ghost imaging algorithm based on CC(cake cutting)sequence is employed to reconstruct low quality images under low sampling conditions, which are then enhanced by SUNet. Experimental results show that, compared to the GIDC(ghost imaging using deep neural network constraint)method proposed in 2022, this approach achieves 3.29 dB improvement in peak signaltonoise ratio and 8% increase in structural similarity at 0.1 sampling rate, providing a new technological avenue for spatial detection in singlepixel imaging.

Key words: single pixel imaging, global attention mechanism, image enhancement, space exploration