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Paper · 2303.09757 · CVPR · 2023

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

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

arXiv · PDF · Open in the Atlas

Code that ran

We lifted 6 functions out of this paper's own repositories and ran 6 of them in a sandbox. "Ran" means the function executed on a synthesized input and returned a value. It is not a reproduction of the paper's results.

RepositoryRoleRan
jiaqixuac/MAP-Net canonical 6 of 6
FunctionStatusWhere it lives
bbox2mask Ran jiaqixuac/MAP-Net/mmedit/core/mask.py
code served (permissive licence) · get_code("d538533133adaba1")
brush_stroke_mask Ran jiaqixuac/MAP-Net/mmedit/core/mask.py
code served (permissive licence) · get_code("d68635a8d8e5f4b5")
flow_warp_5d Ran jiaqixuac/MAP-Net/mmedit/models/backbones/map_backbones/map_utils.py
code served (permissive licence) · get_code("e14c8a75848aac71")
nchw_to_nlc Ran jiaqixuac/MAP-Net/mmedit/models/backbones/map_backbones/map_utils.py
code served (permissive licence) · get_code("f7a6c2f2d2956250")
nlc_to_nchw Ran jiaqixuac/MAP-Net/mmedit/models/backbones/map_backbones/map_utils.py
code served (permissive licence) · get_code("4633d201072bf651")
tensor2img Ran jiaqixuac/MAP-Net/mmedit/core/misc.py
code served (permissive licence) · get_code("dcecea617ace8bea")

Repositories linked to this paper

Some links come from the archived Papers with Code dataset (CC BY-SA 4.0): attribution and licence.

Abstract

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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