@inproceedings{bd4b53de483147c0add5fc0f3c1b062b,
title = "Single image dehazing via multi-scale convolutional neural networks",
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.",
keywords = "Convolutional neural network, Defogging, Image dehazing",
author = "Wenqi Ren and Si Liu and Hua Zhang and Jinshan Pan and Xiaochun Cao and Yang, \{Ming Hsuan\}",
note = "Publisher Copyright: {\textcopyright} Springer International Publishing AG 2016.; 14th European Conference on Computer Vision, ECCV 2016 ; Conference date: 08-10-2016 Through 16-10-2016",
year = "2016",
doi = "10.1007/978-3-319-46475-6\_10",
language = "英语",
isbn = "9783319464749",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Verlag",
pages = "154--169",
editor = "Bastian Leibe and Nicu Sebe and Max Welling and Jiri Matas",
booktitle = "Computer Vision - 14th European Conference, ECCV 2016, Proceedings",
address = "德国",
}