Adaptive locating foveated ghost imaging based on affine transformation.

Ghost imaging (GI) has been widely used in the applications including spectral imaging, 3D imaging, and other fields due to its advantages of broad spectrum and anti-interference. Nevertheless, the restricted sampling efficiency of ghost imaging has impeded its extensive application. In this work, we propose a novel foveated pattern affine transformer method based on deep learning for efficient GI. This method enables adaptive selection of the region of interest (ROI) by combining the proposed retina affine transformer (RAT) network with minimal computational and parametric quantities with the foveated speckle pattern. For single-target and multi-target scenarios, we propose RAT and RNN-RAT (recurrent neural network), respectively. The RAT network enables an adaptive alteration of the fovea of the variable foveated patterns spot to different sizes and positions of the target by predicting the affine matrix with a minor number of parameters for efficient GI. In addition, we integrate a recurrent neural network into the proposed RAT to form an RNN-RAT model, which is capable of performing multi-target ROI detection. Simulations and experimental results show that the method can achieve ROI localization and pattern generation in 0.358 ms, which is a 1 × 105 efficiency improvement compared with the previous methods and improving the image quality of ROI by more than 4 dB. This approach not only improves its overall applicability but also enhances the reconstruction quality of ROI. This creates additional opportunities for real-time GI.

Advances in Ghost Imaging of Moving Targets: A Review.

Ghost imaging is a novel imaging technique that utilizes the intensity correlation property of an optical field to retrieve information of the scene being measured. Due to the advantages of simple structure, high detection efficiency, etc., ghost imaging exhibits broad application prospects in the fields of space remote sensing, optical encryption transmission, medical imaging, and so on. At present, ghost imaging is gradually developing toward practicality, in which ghost imaging of moving targets is becoming a much-needed breakthrough link. At this stage, we can improve the imaging speed and improve the imaging quality to seek a more optimized ghost imaging scheme for moving targets. Based on the principle of moving target ghost imaging, this review summarizes and compares the existing methods for ghost imaging of moving targets. It also discusses the research direction and the technical challenges at the current stage to provide references for further promotion of the instantiation of ghost imaging applications.