Single Image De-Raining with High-Low Frequency Guidance
Ying Zhang, Youjun Xiang, Lei Cai, Yuli Fu, Wanliang Huo, Junjun Xia
Abstract
Rain removal is a highly demanding task because a rainy image in computer lacks discriminative information to distinguish the image details from the rain streaks. In this paper, we present a new High-Low-Frequency Guided De-raining (HLFGD) method to remove the rain streaks clearly while reserve the image details. Specifically, the proposed HLFGD is built with three network branches, namely global-structure branch, de-raining branch, and edge-detail branch, which achieve the collaboration by concatenating intermediate features. Among them, the global-structure and edge-detail branches aim to explore the high-low frequency information, and the de-raining branch leverages the resulting spatial frequency information to restore the global structure of image and to retain fine edge details of objects during the de-raining process. Besides, a new architecture unit, called Residual Co-ordinate Attention Block (RCAB), is proposed to improve the effect of rain removal. Experimental results show the superiority of our method for image de-raining quantificationally and qualitatively.
BibTeX
@inproceedings{icassp2022_singleimagederai,
title = {Single Image De-Raining with High-Low Frequency Guidance},
author = {Ying Zhang and Youjun Xiang and Lei Cai and Yuli Fu and Wanliang Huo and Junjun Xia},
booktitle = {ICASSP 2022},
year = {2022}
}