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Video Dehazing via a Multi-Range Temporal Alignment Network With Physical Prior

Jiaqi Xu · Xiaowei Hu · Lei Zhu · Qi Dou · Jifeng Dai · Yu Qiao · Pheng-Ann Heng

West Building Exhibit Halls ABC 150


Video dehazing aims to recover haze-free frames with high visibility and contrast. This paper presents a novel framework to effectively explore the physical haze priors and aggregate temporal information. Specifically, we design a memory-based physical prior guidance module to encode the prior-related features into long-range memory. Besides, we formulate a multi-range scene radiance recovery module to capture space-time dependencies in multiple space-time ranges, which helps to effectively aggregate temporal information from adjacent frames. Moreover, we construct the first large-scale outdoor video dehazing benchmark dataset, which contains videos in various real-world scenarios. Experimental results on both synthetic and real conditions show the superiority of our proposed method.

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