基于HSV透射率加权修正的机载视频去雾系统设计

针对无人机机载雾天实时获取图像降质的问题，设计一种基于HSV （hue，saturation，value）透射率加权修正的无人机视频去雾处理系统. 首先，根据机载低功耗实时去雾系统要求，完成去雾系统总体设计；其次，根据去雾系统视频采集需要，设计数字视频BT.656/BT.1120隔行和逐行处理、视频控制、指令接收处理、TS1601视频去雾算法处理、H.264视频压缩处理和组帧等功能；最后，重点介绍系统去雾处理算法和平台设计、去雾参数化处理等功能模块的设计实现，分别使用本系统方法和相关文献方法对典型含雾图像处理，采用方差函数、平均梯度函数、TenenGrad函数等3种清晰度评价函数并做归一化处理，以进行客观评价. 研究结果表明:根据HSV分量透射率加权修正的无人机机载图像去雾处理系统设计，具有功耗小、易实现和适应性强等特点；对典型含雾图像3处理后方差函数归一化分别提高46.87%、1.44%、12.83%，平均梯度函数归一化分别提高12.54%、9.26%、11.15%，TenenGrad函数归一化分别提高53.19%、3.60%、8.82%，测试算法整体运算时间分别提高4.74、5.41倍和5.46倍. 

Design of Airborne Video Dehazing System for UCAV Based on HSV Transmission Weighted Correction

As there is image degradation when the UCAV (unmanned combat air vehicle) captures the real-time image in the haze day, a low power-consumption system is designed on the basis of the HSV (hue, saturation, value) transmission weighted correction. First, the overall design of the dehazing system is completed, which can meet the requests of a low power-consumption and real-time image dehazing. Then, according to the needs of the video acquisition dehazing system, functions are being designed, including digital video BT.656/BT.1120 interlaced and progressive processing, video control, instruction receiving processing, TS1601 video dehazing algorithm processing, H.264 video compression processing and framing, etc. Lastly, the design and implementation are focused in terms of the system dehazing algorithm, platform design, dehazing parameter processing and other functional modules. Also this system and several other methods suggested in references are used to process typical hazy images respectively, and then evaluated after employing three definition evaluation functions (variance function, average gradient function and TenenGrad function) and normalization process. The results indicate that the design of this dehazing system has such merits as low power consumption, easy implementation, and high adaptability. After processing the typical hazy images, the variance function is increased by 46.87%, 1.44% and 12.83%, the average gradient function is increased by 12.54%, 9.26% and 11.15%, that of normalization of TenenGrad function is increased by 53.19%, 3.60% and 8.82%, respectively. The overall operation time of the test algorithm is respectively increased by 4.74 times, 5.41 times and 5.46 times.