Underwater detection has always been a challenge due to the limitations caused by scattering and absorption in the underwater environment. Because of their great penetration abilities, lasers have become the most suitable technology for underwater detection. In all underwater laser applications, the reflected laser pulse which contains the key information for most of the system is highly degraded along the laser’s propagation path and during reflection. This has a direct impact on the system’s performance, especially for single-pixel imaging (SPI) which is very dependent on light-intensity information. Due to the complications in the underwater environment, it is necessary to study the influential factors and their impacts on underwater SPI. In this study, we investigated the influence of the angle of incidence, target distance, and medium attenuation. A systematic investigation of the influential factors on the reflectance and ranging accuracy was performed theoretically and experimentally. The theoretical analysis was demonstrated based on the bidirectional reflection distribution function (BRDF) and laser detection and ranging (LADAR) model. Moreover, 2D single-pixel imaging (SPI) systems were setup for experimental investigation. The experimental results agree well with the theoretical results, which show the system’s dependency on the reflection intensity caused by the angle of incidence, target distance, and medium attenuation. The findings should be a reference for works looking to improve the performance of an underwater SPI system.

Attenuation of the laser beam in underwater transmission and detection due to absorption and scattering results in a rapid reduction in energy and blurring of the image. By combining the bidirectional reflectivity distribution function (BRDF) with the Monte Carlo (MC) method, a full-link underwater imaging process model was established which comprehensively investigated the influence of water quality, transmission distance and target characteristics on imaging performance. In order to describe the transmission process of the light more accurately, by adding particles with both absorption and scattering functions in the medium, the Mie scattering theory was employed to simulate the real channel. Moreover, while setting the gate width, the pre-calibrated detector response curve was employed to build a corresponding relationship between the image grayscale and the detector collection energy, aiming to simulate the working mode of the detector in the experiment. In various imaging scenarios, the maximum relative errors between the simulated images and experimental results were within 30%, which proved the correctness of the imaging simulation model and the feasibility of the imaging MC (IMC) method to evaluate the quality of whole imaging process.

Underwater imaging has always been a challenge due to limitations imposed by scattering and absorption nature of the underwater environment. The light would be highly degraded after reflection and propagation in the water medium. Being an advanced imaging technique, Single-pixel Imaging (SPI) is applicable to acquire object spatial information in low light, severe backscattering, and high absorption conditions. Combination of Compressive Sensing (CS) and SPI can overcome the limitation of SPI algorithms such as long data-acquisition time, low reconstruction efficiency and poor reconstruction quality. In the current research, an underwater SPI system based on CS is established to reconstruct our two-dimensional (2D) transparent object. We have systematically investigated the influence of water turbid degree, measurement pattern types and number of measurements on image reconstruction performance. The proposed system is capable to reconstruct the object even when the turbidity reaches up to 80 Nephelometric Turbidity Unit (NTU), where the conventional imaging systems are unusable. Proposed reconstruction method in our research can save more than 70% data acquisition time, compared to SPI algorithm. Our experimental setup has been compared to a conventional imaging system and an underwater ghost imaging system to show its efficiency in obtaining accurate results from turbid water conditions. Furthermore, various algorithm comparison and imaging enhancement studies demonstrates that our algorithm is superior in bringing highly convex optimization at a faster rate with a smaller number of measurements. This work creates new insight into the SPI application and generates a guideline for researchers to improve their applications.

To address scale-like errors in local point clouds caused by pitch angle errors in unmanned aerial vehicle-borne LiDAR bathymetry (UAV-ALB) systems, this study proposes a correction method based on scanline keypoint constraints. First, the point cloud is traversed using scanline information to extract geometric keypoint features from each scanline. Next, a scale-like error correction model is constructed based on the distance differences between keypoints at the start and end of overlapping scanlines, and the pitch angle correction value is estimated using a forward–backward fusion approach. The estimated correction is then applied to the attitude data, and the point cloud is reprocessed. This procedure is iterated until the convergence criterion is satisfied. Experimental results show that, after correction, the horizontal root mean square errors (RMSE) of the two datasets decrease from 0.217 and 0.283 m to 0.025 and 0.030 m, respectively, while the elevation RMSE values decrease from 0.227 and 0.241 m to 0.006 and 0.004 m, respectively. These results demonstrate that the proposed method effectively mitigates scale-like errors in UAV-ALB point clouds and substantially improves their geometric consistency and elevation accuracy.