  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.
