Wide-field scanning ghost imaging based on a local binary pattern and untrained neural network

Continuous scene imaging is an important research goal in the field of autonomous driving, and the key is to ensure the imaging quality and efficiency. In this paper, we propose a method for information fusion in wide-field scanning ghost imaging using a local binary pattern (LBP) based on deep learning. The initial physical model formed by the LBP integrated into a deep neural network, which effectively enhances the expression of image texture details. Then the collected bucket signals are used as labels for adaptive image reconstruction, enabling the acquisition of images at each scanning position without the need for training on any dataset. Moreover, by employing weighted fusion to combine the image data from each scanning position, which effectively eliminates gaps that arise from direct stitching. Both simulation and experimental results demonstrate that our approach is capable of achieving high-quality detailed imaging with fewer measurements. Additionally, we analyze the impact of the projection beam step length, finding that our method yields significantly better imaging quality with larger steps compared to other methods using smaller steps. Our research also has the application prospect in medical detection, remote sensing and other fields.