梁越,
柳晓鸣,
胡青
大连海事大学信息科学技术学院 大连 116026
基金项目:中央高校基本科研业务费专项资金(3132020208)，国家自然科学基金(31700742)
详细信息
作者简介:姚婷婷：女，1988年生，讲师，研究方向为计算机视觉与图像处理等
梁越：男，1996年生，硕士生，研究方向为雾天视频处理
柳晓鸣：男，1959年生，教授，研究方向为海上交通电子信息处理、雷达信号处理等
胡青：男，1978年生，教授，研究方向为海事信息传输、自动识别系统等
通讯作者:姚婷婷 ytt1030@dlmu.edu.cn
1) SfM算法程序可以从网址: http://ccwu.me/vsfm/获得中图分类号:TN911.73, TP391
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被引次数:0
出版历程
收稿日期:2019-06-05
修回日期:2020-06-22
网络出版日期:2020-07-17
刊出日期:2020-11-16
Video Dehazing Algorithm via Haze-line Prior with Spatiotemporal Correlation Constraint
Tingting YAO,,Yue LIANG,
Xiaoming LIU,
Qing HU
College of Information Science and Technology, Dalian Maritime University, Dalian 116026, China
Funds:The Fundamental Research Funds for the Central Universities (3132020208), The National Natural Science Foundation of China (31700742)
摘要
摘要:现有视频去雾算法由于缺少对视频结构关联约束和帧间一致性分析，容易导致连续帧去雾结果在颜色和亮度上存在突变，同时去雾后的前景目标边缘区域也容易出现退化现象。针对上述问题，该文提出一种基于雾线先验的时空关联约束视频去雾算法，通过引入每帧图像在空间邻域中具有的结构关联性和时间邻域中具有的连续一致性，提高视频去雾算法的求解准确性和鲁棒性。算法首先使用暗通道先验估计每帧图像的大气光向量，并结合雾线先验求取初始透射率图。然后引入加权最小二乘边缘保持平滑滤波器对初始透射率图进行空间平滑，消除奇异点和噪声对估计结果的影响。进一步利用相机参数刻画连续帧间透射率图的时序变化规律，对独立求取的每帧透射率图进行时序关联修正。最后根据雾图模型获得最终的视频去雾结果。定性和定量的对比实验结果表明，该算法下视频去雾结果的帧间过渡更加自然，同时对每一帧图像的色彩还原更加准确，图像边缘的细节信息显示也更加丰富。
关键词:视频去雾/
空间平滑/
时序关联约束/
雾线先验
Abstract:Because of the existent video dehazing algorithm lacks the analysis of the video structure correlation constraint and inter-frame consistency, it is easy to cause the dehazing results of continuous frames to have sudden changes in color and brightness. Meanwhile, the edge of foreground target is also prone to degradation. Focus on the aforementioned problems, a novel video dehazing algorithm via haze-line prior with spatiotemporal correlation constraint is proposed, which improves the accuracy and robustness of video dehazing result by bringing the structural relevance and temporal consistency of each frame. Firstly, the dark channel and haze-line prior are utilized to estimate the atmospheric light vector and initial transmission image of each frame. Then a weighted least square edge preserving smoothing filter is introduced to smooth the initial transmission image and eliminate the influence of singularities and noises on the estimated results. Furthermore, the camera parameters are calculated to describe the time series variation of the transmission image between continuous frames, and the independently obtained transmission image of each frame is corrected with temporal correlation constraint. Finally, according to the physical model, the video dehazing results are obtained. The experimental results of qualitative and quantitative comparison show that the proposed algorithm could make the inter-frame transition more smooth, and restore the color of each frame more accurately. Besides, more details are displayed at the edge of the dehazing results.
Key words:Video dehazing/
Spatial smoothing/
Temporal correlation constraint/
Haze-line prior
注释:
1) 1) SfM算法程序可以从网址: http://ccwu.me/vsfm/获得
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https://jeit.ac.cn/article/exportPdf?id=47cefc57-017d-4dd2-a0e8-3325590eed9f
