Design of Airborne Video Dehazing System for UCAV Based on HSV Transmission Weighted Correction
WANG Jian1,2, QIN Chunxia1, YANG Ke1, REN Ping1, ZHENG Jie1,3, ZHAO Yuanpeng2, CHEN Guifeng4
1. Electronic and Information College, Northwestern Polytechnical University, Xi’an 710129, China; 2. No.365 Institute, Northwestern Polytechnical University, Xi’an 710065, China; 3. Shanxi Fenxi Heavy Industry Company of Limited Liability, Taiyuan 030027,China; 4. Maintence Company of State Grid Shaanxi Electric Power Company, Xi’an 710000, China
Abstract：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.
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