Improved Dark Channel Prior Algorithm Based on Wavelet Decomposition for Haze Removal in Dynamic Recognition

Environmental perception and precise positioning of area targets are key technologies in dynamic recognition. However, the perceptual information that is dynamically acquired in some haze weather cannot provide an accurate basis for decision-making and dynamic planning. In addition, under some outdoor condition, such as in the bright light, the quality of the perception information obtained by the system is usually low, and the robustness of the area target is relatively poor. In this paper, the implementation strategy of haze removal for images is comprehensively studied, and an improved dark channel prior algorithm is accordingly proposed by introducing the wavelet decomposition. The related experimental research is further carried out in the MATLAB development environment. As a result, haze can be effectively removed, and the real time of the improved dark channel prior algorithm can be largely enhanced.