Task-Oriented Network for Image Dehazing

Haze interferes the transmission of scene radiation and significantly degrades color and details of outdoor images. Existing deep neural networks-based image dehazing algorithms usually use some common networks. The network design does not model the image formation of haze process well, which accord...

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Bibliographic Details
Published in:IEEE transactions on image processing Vol. 29; pp. 6523 - 6534
Main Authors: Li, Runde, Pan, Jinshan, He, Min, Li, Zechao, Tang, Jinhui
Format: Journal Article
Language:English
Published: United States IEEE 01.01.2020
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:
Algorithms
Artificial neural networks
Atmospheric modeling
Coders
Convolutional neural networks
Distortion
Haze
Image color analysis
image dehazing
Image restoration
Image transmission
multi-stage dehazing algorithm
Neural networks
Recurrent neural networks
Task analysis
Task-oriented network
ISSN:1057-7149, 1941-0042, 1941-0042
Online Access:Get full text
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Summary:Haze interferes the transmission of scene radiation and significantly degrades color and details of outdoor images. Existing deep neural networks-based image dehazing algorithms usually use some common networks. The network design does not model the image formation of haze process well, which accordingly leads to dehazed images containing artifacts and haze residuals in some special scenes. In this paper, we propose a task-oriented network for image dehazing, where the network design is motivated by the image formation of haze process. The task-oriented network involves a hybrid network containing an encoder and decoder network and a spatially variant recurrent neural network which is derived from the hazy process. In addition, we develop a multi-stage dehazing algorithm to further improve the accuracy by filtering haze residuals in a step-by-step fashion. To constrain the proposed network, we develop a dual composition loss, content-based pixel-wise loss and total variation constraint. We train the proposed network in an end-to-end manner and analyze its effect on image dehazing. Experimental results demonstrate that the proposed algorithm achieves favorable performance against state-of-the-art dehazing methods.
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ISSN:1057-7149
1941-0042
1941-0042
DOI:10.1109/TIP.2020.2991509