Foveated ghost imaging based on deep learning

Abstract Ghost imaging is an unconventional imaging mechanism that utilizes the high-order correlation to reconstruct object’s image. Limited by the maximum refresh rate of DMD or SLM, the sampling efficiency of ghost imaging has been a major obstacle for practical application. In this paper, foveated ghost imaging based on deep learning (DPFGI) is proposed to generate non-uniform resolution speckle patterns according to the object detection results as the fovea point. We combine foveated speckle pattern inspired by the human visual system with GAN-based ghost imaging object detection system to realize selecting the region of interest for foveated imaging intelligently. The simulation and experimental results show that DPFGI can detect objects in undersampled images with higher accuracy and achieve higher PSNR in the fovea region compared with uniform-resolution ghost imaging, which opens new perspectives for more intelligent ghost imaging.