Image Dehazing Algorithm Based on Attentional Feature Fusion and Dense Network

被引：0
作者：
Meng H.-J. [1 ]
Liu P.-Y. [1 ]
Hu Z.-W. [1 ]
机构：
[1] School of Information Science & Engineering, Northeastern University, Shenyang
来源：
Dongbei Daxue Xuebao/Journal of Northeastern University | 2022年 / 43卷 / 12期
关键词：
attentional mechanism; auto encoder; densely connected; dilated convolution; feature fusion;
D O I：
10.12068/j.issn.1005-3026.2022.12.007
中图分类号：
学科分类号：
摘要：
There are problems of distorted colors and blurred edges in the results of the state￣of￣the￣art image dehazing algorithms. For solving the problems,an image dehazing algorithm based on deep learning is proposed. The proposed algorithm consists of two modules: attentional feature fusion module and haze model parameter estimation module. Attentional feature fusion module is designed to extract the color and edge features of hazy images sufficiently. Haze model parameter estimation module based on densely connected dilated convolution auto encoder is used to estimate the parameter of haze model and deal with the network degeneration in image dehazing. Experiments on images with thin haze and thick haze show that the proposed algorithm performs well on image dehazing, and the proposed dehazing algorithm has higher structural similarity (SSIM), lower mean￣square error (MSE), lower edge error eedge than the state￣of￣the￣art image dehazing algorithms. © 2022 Northeastern University. All rights reserved.
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页码：1717 / 1723
页数：6
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