@InProceedings{PatriciaSuarez2021,
author="Patricia Suarez
and Dario Carpio
and Angel Sappa",
title="Non-homogeneous Haze Removal Through a Multiple Attention Module Architecture",
booktitle="16th International Symposium on Visual Computing",
year="2021",
volume="13018",
pages="178--190",
abstract="This paper presents a novel attention based architecture to remove non-homogeneous haze. The proposed model is focused on obtaining the most representative characteristics of the image, at each learning cycle, by means of adaptive attention modules coupled with a residual learning convolutional network. The latter is based on the Res2Net model. The proposed architecture is trained with just a few set of images. Its performance is evaluated on a public benchmark---images from the non-homogeneous haze NTIRE 2021 challenge---and compared with state of the art approaches reaching the best result.",
optnote="MSIAU",
optnote="exported from refbase (http://refbase.cvc.uab.es/show.php?record=3668), last updated on Tue, 24 May 2022 14:48:57 +0200",
opturl="https://link.springer.com/chapter/10.1007/978-3-030-90436-4_14"
}