Video Dehazing Algorithm via Haze-line Prior with Spatiotemporal Correlation Constraint

Tingting YAO; Yue LIANG; Xiaoming LIU; Qing HU

doi:10.11999/JEIT190403

Volume 42 Issue 11

Nov. 2020

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Article Navigation > Journal of Electronics & Information Technology > 2020 > 42(11): 2796-2804

Tingting YAO, Yue LIANG, Xiaoming LIU, Qing HU. Video Dehazing Algorithm via Haze-line Prior with Spatiotemporal Correlation Constraint[J]. Journal of Electronics & Information Technology, 2020, 42(11): 2796-2804. doi: 10.11999/JEIT190403

Citation:

Tingting YAO, Yue LIANG, Xiaoming LIU, Qing HU. Video Dehazing Algorithm via Haze-line Prior with Spatiotemporal Correlation Constraint[J]. Journal of Electronics & Information Technology, 2020, 42(11): 2796-2804. doi: 10.11999/JEIT190403

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Video Dehazing Algorithm via Haze-line Prior with Spatiotemporal Correlation Constraint

doi: 10.11999/JEIT190403

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)

Received Date: 2019-06-05
Rev Recd Date: 2020-06-22

Available Online: 2020-07-17

Publish Date: 2020-11-16

Abstract

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.
- Video dehazing,
- Spatial smoothing,
- Temporal correlation constraint,
- Haze-line prior

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References(21)

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