Object identification in computational ghost imaging based on deep learning

Processing method plays an important role in accelerating imaging process in ghost imaging. In this study, we propose a processing method with the Hadamard matrix and a deep neural network called ghost imaging hadamard neural network (GIHNN). We focus on how to break through the bottleneck of image reconstruction time, and GIHNN can identify an object before the imaging process. Our research reveals that the light intensity value contains the feature information of the object and expands the possibility of further applications of artificial intellectual techniques in computational ghost imaging.