The underwater imaging environment is relatively complex. The light in the water has blurred image edges, low overall contrast, and the objects in the water are difficult to recognize. This paper proposes a method based on depth learning and physical model to enhance the quality of underwater image. The enhanced image is obtained by building the module to calculate the important parameters of the model combined with the input. The experimental results show that the algorithm in this paper has faster processing speed and better imaging effect than traditional methods, and can correct the color deviation of underwater images.
In this paper, we study the leader-following consensus problem of general linear multi-agent systems under distributed adaptive event-triggered control. A distributed controller with estimated states is designed, and an adaptive event-triggered communication protocol with auxiliary variables is proposed for each agent to adjust its triggering threshold. Under this control strategy, the system only needs to use the estimated states at the moment of triggering. Compared with the traditional static threshold, the existence of dynamic threshold reduces the number of triggers. Also, an estimator is designed between triggering moments to reduce the state deviation. The next state of each agent depends on local information about itself and its neighbors rather than global information. In addition, it is shown that the multi-agent systems can reach consensus without Zeno behavior. Finally, the effectiveness and feasibility of the proposed method are verified by numerical simulations.
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