Single image dehazing via multi-scale convolutional neural networks

Wenqi Ren; Si Liu; Hua Zhang; Jinshan Pan; Xiaochun Cao; Ming Hsuan Yang

doi:10.1007/978-3-319-46475-6_10

Single image dehazing via multi-scale convolutional neural networks

Wenqi Ren, Si Liu, Hua Zhang, Jinshan Pan, Xiaochun Cao, Ming Hsuan Yang

College of Computing

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

1104 Citations (Scopus)

Abstract

The performance of existing image dehazing methods is limited by hand-designed features, such as the dark channel, color disparity and maximum contrast, with complex fusion schemes. In this paper, we propose a multi-scale deep neural network for single-image dehazing by learning the mapping between hazy images and their corresponding transmission maps. The proposed algorithm consists of a coarse-scale net which predicts a holistic transmission map based on the entire image, and a fine-scale net which refines results locally. To train the multiscale deep network, we synthesize a dataset comprised of hazy images and corresponding transmission maps based on the NYU Depth dataset. Extensive experiments demonstrate that the proposed algorithm performs favorably against the state-of-the-art methods on both synthetic and real-world images in terms of quality and speed.

Original language	English
Title of host publication	Computer Vision - 14th European Conference, ECCV 2016, Proceedings
Editors	Bastian Leibe, Nicu Sebe, Max Welling, Jiri Matas
Publisher	Springer Verlag
Pages	154-169
Number of pages	16
ISBN (Print)	9783319464749
DOIs	https://doi.org/10.1007/978-3-319-46475-6_10
Publication status	Published - 2016
Event	14th European Conference on Computer Vision, ECCV 2016 - Amsterdam, Netherlands Duration: 2016 Oct 8 → 2016 Oct 16

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	9906 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	14th European Conference on Computer Vision, ECCV 2016
Country/Territory	Netherlands
City	Amsterdam
Period	16/10/8 → 16/10/16

Bibliographical note

Publisher Copyright:
© Springer International Publishing AG 2016.

All Science Journal Classification (ASJC) codes

Theoretical Computer Science
Computer Science(all)

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10.1007/978-3-319-46475-6_10

Cite this

Ren, W., Liu, S., Zhang, H., Pan, J., Cao, X., & Yang, M. H. (2016). Single image dehazing via multi-scale convolutional neural networks. In B. Leibe, N. Sebe, M. Welling, & J. Matas (Eds.), Computer Vision - 14th European Conference, ECCV 2016, Proceedings (pp. 154-169). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 9906 LNCS). Springer Verlag. https://doi.org/10.1007/978-3-319-46475-6_10

Ren, Wenqi ; Liu, Si ; Zhang, Hua et al. / Single image dehazing via multi-scale convolutional neural networks. Computer Vision - 14th European Conference, ECCV 2016, Proceedings. editor / Bastian Leibe ; Nicu Sebe ; Max Welling ; Jiri Matas. Springer Verlag, 2016. pp. 154-169 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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abstract = "The performance of existing image dehazing methods is limited by hand-designed features, such as the dark channel, color disparity and maximum contrast, with complex fusion schemes. In this paper, we propose a multi-scale deep neural network for single-image dehazing by learning the mapping between hazy images and their corresponding transmission maps. The proposed algorithm consists of a coarse-scale net which predicts a holistic transmission map based on the entire image, and a fine-scale net which refines results locally. To train the multiscale deep network, we synthesize a dataset comprised of hazy images and corresponding transmission maps based on the NYU Depth dataset. Extensive experiments demonstrate that the proposed algorithm performs favorably against the state-of-the-art methods on both synthetic and real-world images in terms of quality and speed.",

author = "Wenqi Ren and Si Liu and Hua Zhang and Jinshan Pan and Xiaochun Cao and Yang, {Ming Hsuan}",

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Ren, W, Liu, S, Zhang, H, Pan, J, Cao, X & Yang, MH 2016, Single image dehazing via multi-scale convolutional neural networks. in B Leibe, N Sebe, M Welling & J Matas (eds), Computer Vision - 14th European Conference, ECCV 2016, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 9906 LNCS, Springer Verlag, pp. 154-169, 14th European Conference on Computer Vision, ECCV 2016, Amsterdam, Netherlands, 16/10/8. https://doi.org/10.1007/978-3-319-46475-6_10

Single image dehazing via multi-scale convolutional neural networks. / Ren, Wenqi; Liu, Si; Zhang, Hua et al.
Computer Vision - 14th European Conference, ECCV 2016, Proceedings. ed. / Bastian Leibe; Nicu Sebe; Max Welling; Jiri Matas. Springer Verlag, 2016. p. 154-169 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 9906 LNCS).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

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T1 - Single image dehazing via multi-scale convolutional neural networks

AU - Ren, Wenqi

AU - Liu, Si

AU - Zhang, Hua

AU - Pan, Jinshan

AU - Cao, Xiaochun

AU - Yang, Ming Hsuan

N1 - Publisher Copyright: © Springer International Publishing AG 2016.

PY - 2016

Y1 - 2016

N2 - The performance of existing image dehazing methods is limited by hand-designed features, such as the dark channel, color disparity and maximum contrast, with complex fusion schemes. In this paper, we propose a multi-scale deep neural network for single-image dehazing by learning the mapping between hazy images and their corresponding transmission maps. The proposed algorithm consists of a coarse-scale net which predicts a holistic transmission map based on the entire image, and a fine-scale net which refines results locally. To train the multiscale deep network, we synthesize a dataset comprised of hazy images and corresponding transmission maps based on the NYU Depth dataset. Extensive experiments demonstrate that the proposed algorithm performs favorably against the state-of-the-art methods on both synthetic and real-world images in terms of quality and speed.

AB - The performance of existing image dehazing methods is limited by hand-designed features, such as the dark channel, color disparity and maximum contrast, with complex fusion schemes. In this paper, we propose a multi-scale deep neural network for single-image dehazing by learning the mapping between hazy images and their corresponding transmission maps. The proposed algorithm consists of a coarse-scale net which predicts a holistic transmission map based on the entire image, and a fine-scale net which refines results locally. To train the multiscale deep network, we synthesize a dataset comprised of hazy images and corresponding transmission maps based on the NYU Depth dataset. Extensive experiments demonstrate that the proposed algorithm performs favorably against the state-of-the-art methods on both synthetic and real-world images in terms of quality and speed.

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T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

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BT - Computer Vision - 14th European Conference, ECCV 2016, Proceedings

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ER -

Ren W, Liu S, Zhang H, Pan J, Cao X, Yang MH. Single image dehazing via multi-scale convolutional neural networks. In Leibe B, Sebe N, Welling M, Matas J, editors, Computer Vision - 14th European Conference, ECCV 2016, Proceedings. Springer Verlag. 2016. p. 154-169. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-319-46475-6_10

Single image dehazing via multi-scale convolutional neural networks

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