Ghost imaging is widely used in underwater active optical imaging because of its simple structure, long distance, and non-local imaging. However, the complexity of the underwater environment will greatly reduce the imaging quality of ghost imaging. To solve this problem, an underwater ghost imaging method based on the generative adversarial networks is proposed in the study. The generator of the proposed network adopts U-Net with the double skip connections and the attention module to improve the reconstruction quality. In the network training process, the total loss function is the sum of the weighted adversarial loss, perceptual loss, and pixel loss. The experiment and simulation results show that the proposed method effectively improves the target reconstruction performance of underwater ghost imaging. The proposed method promotes the further development of active optical imaging of underwater targets based on ghost imaging technology.

A single photon avalanche diode (SPAD) is a high sensitivity detector that can work under weak echo signal conditions (≤1 photon per pixel). The measured digital signals can be used to invert the range and reflectivity images of the target with photon-efficient imaging reconstruction algorithm. However, the existing photon-efficient imaging reconstruction algorithms are susceptible to noise, which leads to poor quality of the reconstructed range and reflectivity images of target. In this paper, a non-local sparse attention encoder (NLSA-Encoder) neural network is proposed to extract the 3D information to reconstruct both the range and reflectivity images of target. The proposed network model can effectively reduce the influence of noise in feature extraction and maintain the capability of long-range correlation feature extraction. In addition, the network is optimized for reconstruction speed to achieve faster reconstruction without performance degradation, compared with other existing deep learning photon-efficient imaging reconstruction methods. The imaging performance is verified through numerical simulation, near-field indoor and far-field outdoor experiments with a 64 × 64 SPAD array. The experimental results show that the proposed network model can achieve better results in terms of the reconstruction quality of range and reflectivity images, as well as reconstruction speed.

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.

Single photon imaging integrates advanced single photon detection technology with Laser Radar (LiDAR) technology, offering heightened sensitivity and precise time measurement. This approach finds extensive applications in biological imaging, remote sensing, and non-visual field imaging. Nevertheless, current single photon LiDAR systems encounter challenges such as low spatial resolution and a limited field of view in their intensity and range images due to constraints in the imaging detector hardware. To overcome these challenges, this study introduces a novel deep learning image stitching algorithm tailored for single photon imaging. Leveraging the robust feature extraction capabilities of neural networks and the richer feature information present in intensity images, the algorithm stitches range images based on intensity image priors. This innovative approach significantly enhances the spatial resolution and imaging range of single photon LiDAR systems. Simulation and experimental results demonstrate the effectiveness of the proposed method in generating high-quality stitched single-photon intensity images, and the range images exhibit comparable high quality when stitched with prior information from the intensity images.

Image dehazing has always been one of the main areas of research in image processing. The traditional dark channel prior algorithm (DCP) has some shortcomings, such as incomplete fog removal and excessively dark images. In order to obtain haze-free images with high quality, a hybrid dark channel prior (HDCP) algorithm is proposed in this paper. HDCP first employs Retinex to remove the interference of the illumination component. The variant genetic algorithm (VGA) is then used to obtain the guidance image required by the guided filter to optimize the atmospheric transmittance. Finally, the modified dark channel prior algorithm is used to obtain the dehazed image. Compared with three other modified DCP algorithms, HDCP has the best subjective visual effects of haze removal and color fidelity. HDCP also shows superior objective indexes in the mean square error (MSE), peak signal-to-noise ratio (PSNR), structural similarity index (SSIM), and information entropy (E) for different haze degrees.