Single image dehazing via multi-scale convolutional neural networks

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

Research output: Chapter in Book/Report/Conference proceedingConference contribution

825 Citations (Scopus)


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 languageEnglish
Title of host publicationComputer Vision - 14th European Conference, ECCV 2016, Proceedings
EditorsBastian Leibe, Nicu Sebe, Max Welling, Jiri Matas
PublisherSpringer Verlag
Number of pages16
ISBN (Print)9783319464749
Publication statusPublished - 2016
Event14th European Conference on Computer Vision, ECCV 2016 - Amsterdam, Netherlands
Duration: 2016 Oct 82016 Oct 16

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume9906 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference14th European Conference on Computer Vision, ECCV 2016

Bibliographical note

Funding Information:
This work is supported by National High-tech R&D Program of China (2014BAK11B03), National Basic Research Program of China (2013CB329305), National Natural Science Foundation of China (No. 61422213), “Strategic Priority Research Program” of the Chinese Academy of Sciences (XDA06010701), and National Program for Support of Top-notch Young Professionals. W. Ren is supported by a scholarship from China Scholarship Council. M.-H. Yang is supported in part by the NSF CAREER grant #1149783, and gifts from Adobe and Nvidia.

Publisher Copyright:
© Springer International Publishing AG 2016.

All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • Computer Science(all)


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