This paper is concerned with the mitigation of backscatter effects in a single gated image. A range-intensity-profile prior dehazing method is proposed to estimate scene depth and finely remove water backscatter at different depths for underwater range-gated imaging. It is based on the prior that the target intensity is distributed with range intensity profiles in gated images. The depth transmission and depth-noise map are then calculated from the scene depth. A high-quality image is restored by subtracting the depth-noise map and dividing the depth transmission. The simulation and experimental results show that the proposed method works well even if a portion of the estimated depth may be smaller than its real value, and the peak signal-to-noise ratio of dehazing images gets up to a doubled increase.

A smart vector-inspired optical vision guiding (VIOVG) method for autonomous underwater vehicle (AUV) docking and formation is proposed. Unlike traditional optical guiding methods based on LED arrays, the method is inspired by a vector, and uses four laser diodes to form a wing-light pattern to realize smart optical guiding for AUVs. Due to the light scattering effect from water, the four laser diodes can emit four bright and slim laser beams in water, which can be captured by underwater cameras. The intersections of the laser beams form wing-lights as markers for AUV distance and pose estimation. The wing-light pattern is easily tuned and extended by changing the propagation direction and the power of the laser beams beyond the limitation of the physical size of the AUV or docking station. The simulations and experiments show that the proposed method can achieve high precision positioning. A relative distance error of 3.35% is achieved in a positioning experiment at ∼10 m. It has great potential for AUV docking and formation, especially for small AUVs.

Three-dimensional (3D) range-gated imaging can obtain high spatial resolution intensity images as well as pixel-wise depth information. Several algorithms have been developed to recover depth from gated images such as the range-intensity correlation algorithm and deep-learning-based algorithm. The traditional range-intensity correlation algorithm requires specific range-intensity profiles, which are hard to generate, while the existing deep-learning-based algorithm requires large number of real-scene training data. In this work, we propose a method of range-intensity-profile-guided gated light ranging and imaging to recover depth from gated images based on a convolutional neural network. In this method, the range-intensity profile (RIP) of a given gated light ranging and imaging system is obtained to generate synthetic training data from Grand Theft Auto V for our range-intensity ratio and semantic network (RIRS-net). The RIRS-net is mainly trained on synthetic data and fine-tuned with RIP data. The network learns both semantic depth cues and range-intensity depth cues in the synthetic data, and learns accurate range-intensity depth cues in the RIP data. In the evaluation experiments on both a real-scene and synthetic test dataset, our method shows a better result compared to other algorithms.

Three-dimensional laser gated range-intensity correlation imaging (GRICI) can obtain long-range 3D images in real time with high resolution by suppressing backscatter and background noise outside the 3D depth of field. Range precision is crucial for evaluating the performance of 3D GRICI. However, traditional range precision models can only estimate it from experimental results, or cannot accurately predict it theoretically due to over-simplified analysis or insufficient theoretical modeling capability. In this paper, we present a range precision prediction model for 3D GRICI based on the law of error propagation and stochastic process analysis. Particularly, we take into account the photo-electronic process and the temporal jitter of a 3D GRICI system. Based on the classic laser range-gated imaging model, the signal-to-noise model, and the compound random distribution, the mean intensity value and the shot noise can be solved. Thus, the range precision within the 3D depth of field can be accurately predicted. We conducted experiments to validate this model. The results demonstrate that its predictions are more consistent with the experimental results compared to the traditional models. The relative error of the traditional models at 13.45 m is 76.9%, while the relative error of the proposed prediction model is only 3.1%. This research can support the design of 3D gated imaging systems.

In recent years, advanced adiabatic compressed air energy storage (AA-CAES) system has been greatly developed in the energy storage areas, but its performance is limited by the gradual decrease of air temperature in the gas storage tank during the process of energy release. In order to further improve the performance of AA-CAECS system, this paper proposes a method of adjusting air temperature in gas storage by turbine waste heat. On this basis, this paper establishes a novel AA-CAES system(N-AA-CAES) that uses turbine waste heat to adjust the temperature of gas storage tank and establishes thermodynamic model of the N-AA-CAES. Combined with the thermodynamic analysis, the method of adjusting air temperature in gas storage by turbine waste heat can effectively improve the performance of N-AA-CAES system. Case study shows that, compared with AA-CAES, the electric-electric conversion efficiency of N-AA-CAES system is increased by 10.55%, the energy storage density of N-AACAES system is increased by 10.58%.