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.
@article{arxiv.2303.09757,
title = {Video Dehazing via a Multi-Range Temporal Alignment Network with Physical Prior},
author = {Jiaqi Xu and Xiaowei Hu and Lei Zhu and Qi Dou and Jifeng Dai and Yu Qiao and Pheng-Ann Heng},
journal= {arXiv preprint arXiv:2303.09757},
year = {2023}
}