Images captured under poor illumination conditions often exhibit characteristics such as low brightness, low contrast, a narrow gray range, and color distortion, as well as considerable noise, which seriously affect the subjective visual effect on human eyes and greatly limit the performance of various machine vision systems. The role of low-light image enhancement is to improve the visual effect of such images for the benefit of subsequent processing. This paper reviews the main techniques of low-light image enhancement developed over the past decades. First, we present a new classification of these algorithms, dividing them into seven categories: gray transformation methods, histogram equalization methods, Retinex methods, frequency-domain methods, image fusion methods, defogging model methods and machine learning methods. Then, all the categories of methods, including subcategories, are introduced in accordance with their principles and characteristics. In addition, various quality evaluation methods for enhanced images are detailed, and comparisons of different algorithms are discussed. Finally, the current research progress is summarized, and future research directions are suggested.

This paper investigates the performance-optimized tracking control problem of the heterogeneous system consisting of the unmanned surface vehicle (USV) and the unmanned aerial vehicle (UAV) under uncertain dynamics. Based on the target tracking strategy, a performance optimization control framework for cooperative tracking in sea-air 3D space is established. To avoid the singularity of the UAV in the position loop inverse solution of the desired attitude, the saturation control strategy is designed to solve the UAV guidance problem, and the UAV position loop obtains bounded thrust. For the kinematic layer of the USV-UAV system, based on the prescribed performance control and optimal control methods, a prescribed performance-optimized control strategy is designed to optimize the virtual control input while ensuring the prescribed control performance. In addition, to reduce the impact of uncertainty on the USV-UAV control system, a prescribed performance compensation control scheme is proposed in the kinetic loop, which improves the USV-UAV control system robustness. The established sea-air 3D cooperative target tracking control framework can also be extended to complex formation control. Finally, experimental simulations verify the effectiveness of the designed control scheme.

This study addresses the formation control problem of under-actuated unmanned surface vehicle (USV) swarm systems and designs an obstacle avoidance strategy for navigating in obstacle environments. The intermittent communication scheme, consisting of the working period and the rest period, is introduced to reduce the communication burden and the consumption of control resources. A distributed formation control strategy is designed to guarantee the realization of the desired formation configuration. Considering the potential impact of obstacles in the operating environment, the artificial potential field repulsion obstacle avoidance method is embedded into the formation control strategy. To eliminate the impact of exogenous disturbances, a disturbance observer is established using the partially known disturbance information. Sufficient conditions are provided to ensure the uniformly ultimate boundedness of the formation error and disturbance observation error. In addition, an energy function related to the obstacle position information is designed to verify that the desired obstacle avoidance behavior can be achieved. The feasibility of theoretical analysis is validated by applying the developed control strategy to surface formation cruise missions.

Due to the scattering of atmospheric particles, images captured under hazy conditions suffer from contrast attenuation and color distortion, which severely affect the performance of machine vision systems. Various types of methods have been developed to improve the clarity of images. However, these methods are typically challenging to apply in real-time systems. We present a fast, single-image dehazing method based on the atmospheric scattering theory and dark channel prior theory. The transmission map is approximately estimated using a fast average filter, the subsection mechanism is designed to avoid the high brightness of the sky region in the recovered image, the region projection method is adopted to obtain the atmospheric light, and image color compensation is implemented using the Weber-Fechner law. Our experimental results show that this algorithm can restore images to a clear and natural state and ensure the balance of quality and the speed of image restoration. Therefore, the algorithm can be used in real-time systems.