Global ghost imaging

Ghost imaging, as an imaging technique, holds great potential for standard imaging. However, the inability to achieve a large field of view, high resolution, and high-quality image reconstruction in a short time with a small number of measurements seriously hinders the practical application of ghost imaging. Parallel ghost imaging treats each pixel of the position-sensitive detector as a bucket detector and simultaneously executes tens of thousands of ghost imaging processes in parallel. This enables nonlocal imaging with high resolution, an extra-large field of view, and low dosage. In this work, we propose a dedicated imaging method for parallel ghost imaging within the framework of the bucket detector array, namely global ghost imaging. Global ghost imaging introduces global prior knowledge, enabling parallel ghost imaging not to be calculated independently within each local system but to have the global prior cover all subsystems. The speckle patterns of each ghost imaging subsystem are uploaded to the terminal for unified iterative computation. This transforms the iterative sparse solutions of each subsystem from local optima to a global optimum. Simulations and experiments demonstrate that global ghost imaging achieves a large field of view and high-resolution imaging, completely eliminates the discontinuities between subsystems, significantly improves image quality, exhibits strong noise robustness, and, more crucially, enables image reconstruction with an extremely low number of samples. By using the classical ghost imaging framework and the computational ghost imaging framework, respectively, we showcase the ability of this method to reconstruct a complex sample with an image size of 800×280 pixels using only eight measurements.