@inproceedings{3e9274eb32954c0bbec72c9b1084c798,
title = "Gated Contiguous Memory U-Net for Single Image Dehazing",
abstract = "Single image dehazing is a challenging problem that aims to recover a high-quality haze-free image from a hazy image. In this paper, we propose an U-Net like deep network with contiguous memory residual blocks and gated fusion sub-network module to deal with the single image dehazing problem. The contiguous memory residual block is used to increase the flow of information by feature reusing and a gated fusion sub-network module is used to better combine the features of different levels. We evaluate our proposed method using two public image dehazing benchmarks. The experiments demonstrate that our network can achieve a state-of-the-art performance when compared with other popular methods.",
keywords = "Contiguous memory resblock, Gated fusion sub-network, Single image dehazing, U-Net like deep network",
author = "Lei Xiang and Hang Dong and Fei Wang and Yu Guo and Kaisheng Ma",
note = "Publisher Copyright: {\textcopyright} 2019, Springer Nature Switzerland AG.; 26th International Conference on Neural Information Processing, ICONIP 2019 ; Conference date: 12-12-2019 Through 15-12-2019",
year = "2019",
doi = "10.1007/978-3-030-36711-4\_11",
language = "英语",
isbn = "9783030367107",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer",
pages = "117--127",
editor = "Tom Gedeon and Wong, \{Kok Wai\} and Minho Lee",
booktitle = "Neural Information Processing - 26th International Conference, ICONIP 2019, Proceedings",
}