Residual spatial and channel attention networks for single image dehazing

doi:10.3390/s21237922

CIOMP OpenIR

	Residual spatial and channel attention networks for single image dehazing
	X. Jiang; C. Zhao; M. Zhu; Z. Hao and W. Gao
	2021
发表期刊	Sensors
ISSN	14248220
卷号	21 期号:23 页码:916-931
摘要	Single image dehazing is a highly challenging ill-posed problem. Existing methods in-cluding both prior-based and learning-based heavily rely on the conceptual simplified atmospheric scattering model by estimating the so-called medium transmission map and atmospheric light. However, the formation of haze in the real world is much more complicated and inaccurate esti-mations further degrade the dehazing performance with color distortion, artifacts and insufficient haze removal. Moreover, most dehazing networks treat spatial-wise and channel-wise features equally, but haze is practically unevenly distributed across an image, thus regions with different haze concentrations require different attentions. To solve these problems, we propose an end-to-end trainable densely connected residual spatial and channel attention network based on the conditional generative adversarial framework to directly restore a haze-free image from an input hazy image, without explicitly estimation of any atmospheric scattering parameters. Specifically, a novel residual attention module is proposed by combining spatial attention and channel attention mechanism, which could adaptively recalibrate spatial-wise and channel-wise feature weights by considering interdependencies among spatial and channel information. Such a mechanism allows the network to concentrate on more useful pixels and channels. Meanwhile, the dense network can maximize the information flow along features from different levels to encourage feature reuse and strengthen feature propagation. In addition, the network is trained with a multi-loss function, in which contrastive loss and registration loss are novel refined to restore sharper structures and ensure better visual quality. Experimental results demonstrate that the proposed method achieves the state-of-the-art performance on both public synthetic datasets and real-world images with more visually pleasing dehazed results. 2021 by the authors. Licensee MDPI, Basel, Switzerland.
DOI	10.3390/s21237922
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收录类别	EI
引用统计
文献类型	期刊论文
条目标识符	http://ir.ciomp.ac.cn/handle/181722/65598
专题	中国科学院长春光学精密机械与物理研究所
推荐引用方式 GB/T 7714	X. Jiang,C. Zhao,M. Zhu,et al. Residual spatial and channel attention networks for single image dehazing[J]. Sensors,2021,21(23):916-931.
APA	X. Jiang,C. Zhao,M. Zhu,&Z. Hao and W. Gao.(2021).Residual spatial and channel attention networks for single image dehazing.Sensors,21(23),916-931.
MLA	X. Jiang,et al."Residual spatial and channel attention networks for single image dehazing".Sensors 21.23(2021):916-931.

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