Video dehazing with spatial and temporal coherence

Authors:
Jiawan Zhang;Liang Li;Yi Zhang;Guoqiang Yang;Xiaochun Cao;Jizhou Sun
Affiliations:
Tianjin University, School of Computer Science and Technology/ MTS Lab/ School of Software, Tianjin, China;Tianjin University, School of Computer Science and Technology, Tianjin, China and Tianjin University, MTS Lab, Tianjin, China;Tianjin University, MTS Lab, Tianjin, China and Tianjin University, School of Software, Tianjin, China;Tianjin University, School of Computer Science and Technology, Tianjin, China and Tianjin University, MTS Lab, Tianjin, China;Tianjin University, School of Computer Science and Technology, Tianjin, China and Tianjin University, MTS Lab, Tianjin, China;Tianjin University, School of Computer Science and Technology, Tianjin, China and Tianjin University, MTS Lab, Tianjin, China
Venue:
The Visual Computer: International Journal of Computer Graphics - CGI'2011 Conference
Year:
2011

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Abstract

This paper describes a new framework for video dehazing, the process of restoring the visibility of the videos taken under foggy scenes. The framework builds upon techniques in single image dehazing, optical flow estimation and Markov random field. It aims at improving the temporal and spatial coherence of the dehazed video. In this framework, we first extract the transmission map frame-by-frame using guided filter, then estimate the forward and backward optical flow between two neighboring frames to find the matched pixels. The flow fields are used to help us building an MRF model on the transmission map to improve the spatial and temporal coherence of the transmission. The proposed algorithm is verified in both real and synthetic videos. The results demonstrate that our algorithm can preserve the spatial and temporal coherence well. With more coherent transmission map, we get better refocusing effect. We also apply our framework on improving the video coherence on the application of video denoising.