Multiple-Image Reconstruction of a Fast Periodic Moving/State-Changed Object Based on Compressive Ghost Imaging

Abstract

We propose a multiple-image reconstruction scheme of a fast periodic moving/state-changed object with a slow bucket detector based on compressive ghost imaging, named MIPO-CSGI. To obtain N frames of an object with fast periodic moving/state-changed, N random speckle patterns are generated in each cycle of the object, which are then used to illuminate the object one by one. The total energy reflected from the object is recorded by a slow bucket detector at each cycle time T. Each group with N random speckle patterns is programmed as one row of a random matrix, and each row of the matrix element corresponds to one measurement of the slow bucket detector. Finally, the compressive sensing algorithm is applied to the constructed matrix and bucket detector signals, resulting in the direct acquisition of multiple images of the object. The feasibility of our method has been demonstrated in both numerical simulations and experiments. Hence, even with a slow bucket detector, MIPO-CSGI can image a fast periodic moving/state-changed object effectively.