FAOD-Net: A Fast AOD-Net for Dehazing Single Image
Joint Authors
Zhang, Dengyin
Qian, Wen
Zhou, Chao
Source
Mathematical Problems in Engineering
Issue
Vol. 2020, Issue 2020 (31 Dec. 2020), pp.1-11, 11 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2020-02-24
Country of Publication
Egypt
No. of Pages
11
Main Subjects
Abstract EN
In this paper, we present an extremely computation-efficient model called FAOD-Net for dehazing single image.
FAOD-Net is based on a streamlined architecture that uses depthwise separable convolutions to build lightweight deep neural networks.
Moreover, the pyramid pooling module is added in FAOD-Net to aggregate the context information of different regions of the image, thereby improving the ability of the network model to obtain the global information of the foggy image.
To get the best FAOD-Net, we use the RESIDE training set to train our proposed model.
In addition, we have carried out extensive experiments on the RESIDE test set.
We use full-reference and no-reference image quality evaluation indicators to measure the effect of dehazing.
Experimental results show that the proposed algorithm has satisfactory results in terms of defogging quality and speed.
American Psychological Association (APA)
Qian, Wen& Zhou, Chao& Zhang, Dengyin. 2020. FAOD-Net: A Fast AOD-Net for Dehazing Single Image. Mathematical Problems in Engineering،Vol. 2020, no. 2020, pp.1-11.
https://search.emarefa.net/detail/BIM-1195525
Modern Language Association (MLA)
Qian, Wen…[et al.]. FAOD-Net: A Fast AOD-Net for Dehazing Single Image. Mathematical Problems in Engineering No. 2020 (2020), pp.1-11.
https://search.emarefa.net/detail/BIM-1195525
American Medical Association (AMA)
Qian, Wen& Zhou, Chao& Zhang, Dengyin. FAOD-Net: A Fast AOD-Net for Dehazing Single Image. Mathematical Problems in Engineering. 2020. Vol. 2020, no. 2020, pp.1-11.
https://search.emarefa.net/detail/BIM-1195525
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1195525