Two-Stage Video De-Raining with Spatio-Temporal Fusion and Illumination-Invariant Detail Preservation
Yufeng Tan, Youjun Xiang, Lei Cai, Pengcheng Wang, Ying Zhang, Yuli Fu
Abstract
Video de-raining is an important yet highly challenging task in the field of computer vision. Though numerous video de-raining methods are developed with encouraging performance, two major challenges for video de-raining are still unsatisfactorily solved and need to be further investigated as follows: 1) how to sufficiently explore the useful spatio-temporal information from adjacent rainy frames to facilitate the rain removal, and 2) how to well preserve background details even in a video with illumination variance. Regarding the above challenges, this paper specifically develops a new two-stage video de-raining method, which cleverly integrates two typical modules that are beneficial for the video de-raining task, namely Spatio-Temporal Fusion (STF) module and Illumination-Invariant Detail Preservation (IIDP) module. The STF module is designed to fuse the spatio-temporal information from successive frames effectively, while the IIDP module is developed to deliver the enhanced features from the first stage sub-network to the second stage sub-network to preserve clear edge details of objects. Experimental results demonstrate the superiority of our proposed method over previous state-of-the-arts. The code will be publicly available at https://github.com/mapleTan1113/TSVDN.
BibTeX
@inproceedings{icassp2023_twostagevideoder,
title = {Two-Stage Video De-Raining with Spatio-Temporal Fusion and Illumination-Invariant Detail Preservation},
author = {Yufeng Tan and Youjun Xiang and Lei Cai and Pengcheng Wang and Ying Zhang and Yuli Fu},
booktitle = {ICASSP 2023},
year = {2023}
}