@inproceedings{999a2dbe11034c8e97265e70a048f2af,
title = "Proximal dehaze-net: A prior learning-based deep network for single image dehazing",
abstract = "Photos taken in hazy weather are usually covered with white masks and often lose important details. In this paper, we propose a novel deep learning approach for single image dehazing by learning dark channel and transmission priors. First, we build an energy model for dehazing using dark channel and transmission priors and design an iterative optimization algorithm using proximal operators for these two priors. Second, we unfold the iterative algorithm to be a deep network, dubbed as proximal dehaze-net, by learning the proximal operators using convolutional neural networks. Our network combines the advantages of traditional prior-based dehazing methods and deep learning methods by incorporating haze-related prior learning into deep network. Experiments show that our method achieves state-of-the-art performance for single image dehazing.",
keywords = "Deep neural network, Prior learning, Single image dehazing",
author = "Dong Yang and Jian Sun",
note = "Publisher Copyright: {\textcopyright} Springer Nature Switzerland AG 2018.; 15th European Conference on Computer Vision, ECCV 2018 ; Conference date: 08-09-2018 Through 14-09-2018",
year = "2018",
doi = "10.1007/978-3-030-01234-2\_43",
language = "英语",
isbn = "9783030012335",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Verlag",
pages = "729--746",
editor = "Vittorio Ferrari and Cristian Sminchisescu and Martial Hebert and Yair Weiss",
booktitle = "Computer Vision – ECCV 2018 - 15th European Conference, 2018, Proceedings",
}