A Dual-Path Computational Ghost Imaging Method Based on Convolutional Neural Networks.

Ghost imaging is a technique for indirectly reconstructing images by utilizing the second-order or higher-order correlation properties of the light field, which exhibits a robust ability to resist interference. On the premise of ensuring the quality of the image, effectively broadening the imaging range can improve the practicality of the technology. In this paper, a dual-path computational ghost imaging method based on convolutional neural networks is proposed. By using the dual-path detection structure, a wider range of target image information can be obtained, and the imaging range can be expanded. In this paper, for the first time, we try to use the two-channel probe as the input of the convolutional neural network and successfully reconstruct the target image. In addition, the network model incorporates a self-attention mechanism, which can dynamically adjust the network focus and further improve the reconstruction efficiency. Simulation results show that the method is effective. The method in this paper can effectively broaden the imaging range and provide a new idea for the practical application of ghost imaging technology.