Underwater ghost imaging based on deep learning can effectively reduce the influence of forward scattering and back scattering of water. With the help of data-driven methods, high-quality results can be reconstructed. However, the training of the underwater ghost imaging requires enormous paired underwater datasets, which are difficult to obtain directly. Although the Cycle-GAN method solves the problem to some extent, the blurring degree of the fuzzy class of the paired underwater datasets generated by Cycle-GAN is relatively unitary. To solve this problem, a few-shot underwater image generative network method is proposed. Utilizing the proposed few-shot learning image generative method, the generated paired underwater datasets are better than those obtained by the Cycle-GAN method, especially under the condition of few real underwater datasets. In addition, to reconstruct high-quality results, an underwater deblurring ghost imaging method is proposed. The reconstruction method consists of two parts: reconstruction and deblurring. The experimental and simulation results show that the proposed reconstruction method has better performance in deblurring at a low sampling rate, compared with existing underwater ghost imaging methods based on deep learning. The proposed reconstruction method can effectively increase the clarity degree of the underwater reconstruction target at a low sampling rate and promotes the further applications of underwater ghost imaging.

The combination of a single-photon avalanche diode detector with a high-sensitivity and photon-efficient reconstruction algorithm can realize the reconstruction of target range image from weak light signal conditions. The limited spatial resolution of the detector and the substantial background noise remain significant challenges in the actual detection process, hindering the accuracy of 3D reconstruction techniques. To address this challenge, this paper proposes a denoising super-resolution reconstruction network based on generative adversarial network (GAN) design. Soft thresholding is incorporated into the deep architecture as a nonlinear transformation layer to effectively filter out noise. Moreover, the Unet-based discriminator is introduced to complete the high-precision detail reconstruction. The experimental results show that the proposed network can achieve high-quality super-resolution range imaging. This approach has the potential to enhance the accuracy and quality of long-range imaging in weak light signal conditions, with broad applications in fields such as robotics, autonomous vehicles, and biomedical imaging.