[1]WANG Dewen,CHEN Wei,SU Pan.Single-image dehazing via a coarse-to-fine multiscale approach[J].CAAI Transactions on Intelligent Systems,2024,19(5):1102-1110.[doi:10.11992/tis.202305005]
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Single-image dehazing via a coarse-to-fine multiscale approach

CAAI Transactions on Intelligent Systems[ISSN 1673-4785/CN 23-1538/TP] Volume: 19 Number of periods: 2024 5 Page number: 1102-1110 Column: 学术论文—机器学习 Public date: 2024-09-05
Title:
Single-image dehazing via a coarse-to-fine multiscale approach
Author(s):
WANG Dewen1; 2;  CHEN Wei1;  SU Pan1; 2
1. School of Control and Computer Engineering, North China Electric Power University, Baoding 071003, China;
2. Engineering Research Center of the Ministry of Education for Intelligent Computing of Complex Energy System, North China Electric Power University, Baoding 071003, China
Keywords:
image dehazing; coarse to fine; multiscale feature fusion; residual feature attention; asymmetric feature fusion; adaptive mixup; contrastive regularization; positive and negative sample
CLC:
TP391
DOI:
10.11992/tis.202305005
Abstract:
A single-image dehazing method based on a coarse-to-fine, multiscale approach is proposed to address the shortcomings of existing dehazing methods, which often result in loss of detailed texture, color distortion, or incomplete processing of non-uniform dense fog. First, features are extracted from foggy images using the residual feature attention module of the backbone network. Second, images at different scales are preprocessed using convolution, and the preprocessed shallow features are fused with the backbone network through a multiscale feature fusion module. Asymmetric features at different levels are then effectively fused. Finally, shallow information is adaptively combined with deep information at the output. Compared with contrastive regularization loss, positive and negative sample information is constructed to make the defogged image more similar to a fog-free image. The experimental results show that, compared with existing representative defogging methods, the proposed method effectively defogs synthetic and real datasets and outperforms comparison methods in terms of detail retention and color reproduction.
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