基于稠密残差块与通道像素注意力的图像去雾网络

doi:10.16182/j.issn1004731x.joss.21-1160

系统仿真学报 ›› 2022, Vol. 34 ›› Issue (8): 1663-1673.doi: 10.16182/j.issn1004731x.joss.21-1160

• 仿真建模理论与方法 • 下一篇

基于稠密残差块与通道像素注意力的图像去雾网络

金炜东¹^,²(), 张述礼¹(), 唐鹏¹, 张曼¹

^1.西南交通大学电气工程学院，四川成都 610031
^2.南宁学院中国-东盟综合交通国际联合实验室，广西南宁 530200

收稿日期:2021-11-11 修回日期:2022-01-24 出版日期:2022-08-30 发布日期:2022-08-15
通讯作者: 张述礼 E-mail:wdjin@home.swjtu.edu.cn;1273438490@qq.com
作者简介:金炜东(1959-)，男，博士，教授，研究方向为智能信息处理、模式识别等。E-mail：wdjin@home.swjtu.edu.cn
基金资助:
国家重点研发计划(2016YFB1200401-102F)

Image Dehazing Network Based on Densely Connected Residual Block and Channel Pixel Attention

Weidong Jin¹^,²(), Shuli Zhang¹(), Peng Tang¹, Man Zhang¹

^1.School of Electrical Engineering, Southwest Jiaotong University, Chengdu 610031, China
^2.China-ASEAN International Joint Laboratory of Integrated Transport, Nanning University, Nanning 530200, China

Received:2021-11-11 Revised:2022-01-24 Online:2022-08-30 Published:2022-08-15
Contact: Shuli Zhang E-mail:wdjin@home.swjtu.edu.cn;1273438490@qq.com

摘要/Abstract

摘要：

为解决复杂的室外图像进行去雾，依然会有雾气残留，甚至出现颜色失真和纹理丢失问题，提出一种基于稠密残差块与通道像素注意力的图像去雾网络，利用稠密残差块对有雾图像进行特征提取和融合，用带通道像素注意力机制的修复模块对特征图进行颜色和纹理上的修复。实验结果表明：该方法在客观评价指标和主观视觉质量上都有明显提升，有效避免了去雾过程中的颜色失真、纹理丢失和雾气残留问题。

关键词: 图像去雾, 稠密残差块, 注意力机制, 颜色失真, 细节纹理

Abstract:

Abstruct: A lot of research achievements have been made in image dehazing based on neural network,but there aiming at the fog residue, even the color distortion and texture loss, in complex outdoor image dehazing, an image dehazing network based on densely connected residual block and channel pixel attention is proposed. Densely connected residual blocks are used to extract and fuse the features of foggy images,and the repair module with channel pixel attention mechanism is used to repair the color and texture of the feature maps. The experimental results show that, compared with the existing methods, the proposed method and significantly improves the objective evaluation index and subjective visual quality, effectively avoid the color distortion, texture loss and residual fog in the process of image dehazing.

Key words: image dehazing, densely connected residual block, attention mechanism, color distortion, detail texture

中图分类号:

TP391.9

金炜东, 张述礼, 唐鹏, 张曼. 基于稠密残差块与通道像素注意力的图像去雾网络[J]. 系统仿真学报, 2022, 34(8): 1663-1673.

Weidong Jin, Shuli Zhang, Peng Tang, Man Zhang. Image Dehazing Network Based on Densely Connected Residual Block and Channel Pixel Attention[J]. Journal of System Simulation, 2022, 34(8): 1663-1673.

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方法	β=0.06		β=0.10		β=0.16		β=0.20		Average
方法	PSNR/dB	SSIM	PSNR/dB	SSIM	PSNR/dB	SSIM	PSNR/dB	SSIM	PSNR/dB	SSIM
DCP	16.25	0.813 3	16.01	0.818 4	15.89	0.799 8	15.91	0.790 7	16.02	0.805 6
CAP	21.61	0.881 9	22.15	0.893 2	21.98	0.874 4	20.77	0.843 2	21.63	0.873 2
MOF	18.57	0.861 2	18.65	0.872 2	18.96	0.857 3	18.63	0.833 3	18.70	0.855 1
MSCNN	20.38	0.851 0	23.61	0 894 1	22.79	0.876 9	20.17	0.838 8	21.74	0.865 2
AOD-Net	21.82	0.880 5	24.80	0.915 7	22.28	0.867 5	19.52	0.836 8	22.11	0.875 1
本文	28.46	0.921 8	27.71	0.920 1	25.34	0.888 5	24.07	0.870 2	26.40	0.900 2

方法	L_dc
DCP	0.136 4
CAP	0.172 5
MOF	0.162 3
MSCNN	0.189 3
AOD-Net	0.187 0
本文	0.149 4

方法	PSNR/dB	SSIM
DRCA-CTR	26.40	0.900 2
RCA-CTR	25.19	0.890 0
DR-CTR	25.54	0.893 2
DRCA	24.90	0.882 4

图像	BGR比重
原始图像	(0.18, 0.36, 0.26)
有雾图像	(0.47, 0.58, 0.51)
DRCA	(0.19, 0.30, 0.21)
DRCA-CTR	(0.19, 0.38, 0.29)

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基于稠密残差块与通道像素注意力的图像去雾网络

Image Dehazing Network Based on Densely Connected Residual Block and Channel Pixel Attention

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