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基于吸收透射率补偿及时空导向图像滤波的实时视频去雾
Alternative TitleReal-Time Video Dehazing Based on Absorption Transmission Compensation and Spatio-Temporal Guided Image Filtering
崔童1,2,3; 田建东1,2; 王强1,2,3; 任卫红1,2,3; 唐延东1,2
Department机器人学研究室
Source Publication机器人
ISSN1002-0446
2019
Pages1-10
Contribution Rank1
Funding Organization国家自然科学基金(91648118,61473280,61333019)
Keyword视频去雾 时空导向图像滤波 亮度饱和度比 吸收透射率补偿
Abstract视频去雾技术的难点主要在于如何保证视频数据的时空一致性,为了解决这一问题提出了时空导向图像滤波优化算法.该算法考虑了视频帧间信息在空间和时间维度上的一致性因素,在平滑透射率纹理并保护显著边界的同时,能够克服视频中的闪烁噪声,保证视频去雾结果的流畅性.由于经典去雾模型仅考虑散射对于雾生成的影响,导致大多数基于该模型的去雾算法在近景处常产生过饱和噪声,针对此问题提出了一个基于吸收透射率补偿的透射率估计算法,弥补了经典模型忽略大气吸收衰减的缺陷,能够显著提高透射率估计精度,有效地抑制近景处过饱和噪声的产生.在真实雾视频和合成雾视频数据上进行了与现有先进算法的对比实验.有参考定量评价结果表明,本文算法的信噪比及结构相似性两项指标分别高于其他算法至少12%和3.4%;无参考的可见边界恢复评价指标至少高于其他算法5.7%.所提出的实时视频去雾算法能够更有效地恢复高频信息,更恰当地提升图像对比度,所获得的无雾视频色彩也更加自然、真实.
Other AbstractThe difficulty of the video dehazing technology is to guarantee the spatial-temporal consistency of the video data. A spatio-temporal guided image filtering (ST-GIF) optimization algorithm is proposed to solve this problem. The spatial and temporal consistencies of the videos’ interframe information are taken into consideration in the algorithm. For one thing, the transmission texture is smoothed and the salient boundary is protected. And for another, the flicker noise in the videos are suppressed to ensure the fluency of the haze-free video. Since the classical dehazing model only concerns the influence of scattering on the fog formation, most of the dehazing algorithms based on this model usually generate over-saturated noise in the close shot. A transmission estimation algorithm based on absorption transmission compensation is proposed to solve this problem. This algorithm overcomes the defect that the classical model ignores atmospheric absorption attenuation, significantly improves the accuracy of transmission estimation, and effectively suppresses the over-saturations in the close shot. The proposed algorithm is experimentally compared with several state-of-the-art algorithms on both real-world and synthetic haze video data. The quantitative evaluation results with reference show that the proposed algorithm is at least 12% and 3.4% higher than the others in the two metrics of the signal-to-noise ratio and the structural similarity respectively. As an evaluation method without reference, the visible boundary restoration metric of the proposed algorithm is at least 5.7% higher than the others. Results demonstrate that the proposed real-time video dehazing algorithm can recover the high frequency information more effectively, and improve the image contrast more properly, and the colours of the obtained dehazing videos are more natural and authentic.
Language中文
Document Type期刊论文
Identifierhttp://ir.sia.cn/handle/173321/25218
Collection机器人学研究室
Corresponding Author田建东
Affiliation1.中国科学院沈阳自动化研究所机器人学国家重点实验室
2.中国科学院机器人与智能制造创新研究院
3.中国科学院大学
Recommended Citation
GB/T 7714
崔童,田建东,王强,等. 基于吸收透射率补偿及时空导向图像滤波的实时视频去雾[J]. 机器人,2019:1-10.
APA 崔童,田建东,王强,任卫红,&唐延东.(2019).基于吸收透射率补偿及时空导向图像滤波的实时视频去雾.机器人,1-10.
MLA 崔童,et al."基于吸收透射率补偿及时空导向图像滤波的实时视频去雾".机器人 (2019):1-10.
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