High-resolution sparse speckle shifting computational ghost imaging

Abstract

Computational ghost imaging has been widely used in low-light night vision and non-line-of-sight imaging due to its speckle computational imaging and single pixel detection. In this manuscript, a sparse speckle-shifting sampling method leading to high-resolution edge images reconstruction is proposed to overcome the limitation of the low resolution of edge detection in computational ghost imaging. Binary sparse speckle groups are generated by edge detection operator and directly computes high-resolution target edge images from a series of bucket-detector measurements. Compared with the traditional method of speckle-shifting ghost imaging, the space complexity of the method in this paper is only 10% of the traditional one and the reconstruction time complexity is only 30% of the traditional one when the spatial resolution is 512×512 pixels. In this manuscript, the feasibility of the method is verified by numerical simulations and real experiments. This method provides new ideas and rationale for the recognition and detection of weak and small targets at long distances by computational ghost imaging.