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		<citationkey>GonçalvesGayaDrewBote:2018:SiImDe</citationkey>
		<author>Gonçalves, Lucas Teixeira,</author>
		<author>Gaya, Joel Felipe de Oliveira,</author>
		<author>Drews-Jr, Paulo Jorge Lilles,</author>
		<author>Botelho, Silvia Silva da Costa,</author>
		<affiliation>Universidade Federal do Rio Grande</affiliation>
		<affiliation>Universidade Federal do Rio Grande</affiliation>
		<affiliation>Universidade Federal do Rio Grande</affiliation>
		<affiliation>Universidade Federal do Rio Grande</affiliation>
		<title>GuidedNet: Single Image Dehazing Using an End-to-end Convolutional Neural Network</title>
		<conferencename>Conference on Graphics, Patterns and Images, 31 (SIBGRAPI)</conferencename>
		<year>2018</year>
		<editor>Ross, Arun,</editor>
		<editor>Gastal, Eduardo S. L.,</editor>
		<editor>Jorge, Joaquim A.,</editor>
		<editor>Queiroz, Ricardo L. de,</editor>
		<editor>Minetto, Rodrigo,</editor>
		<editor>Sarkar, Sudeep,</editor>
		<editor>Papa, João Paulo,</editor>
		<editor>Oliveira, Manuel M.,</editor>
		<editor>Arbeláez, Pablo,</editor>
		<editor>Mery, Domingo,</editor>
		<editor>Oliveira, Maria Cristina Ferreira de,</editor>
		<editor>Spina, Thiago Vallin,</editor>
		<editor>Mendes, Caroline Mazetto,</editor>
		<editor>Costa, Henrique Sérgio Gutierrez,</editor>
		<editor>Mejail, Marta Estela,</editor>
		<editor>Geus, Klaus de,</editor>
		<editor>Scheer, Sergio,</editor>
		<booktitle>Proceedings</booktitle>
		<date>Oct. 29 - Nov. 1, 2018</date>
		<publisheraddress>Los Alamitos</publisheraddress>
		<publisher>IEEE Computer Society</publisher>
		<conferencelocation>Foz do Iguaçu, PR, Brazil</conferencelocation>
		<keywords>deep learning, single image dehazing, convolutional neural networks, guided filter.</keywords>
		<abstract>Poor visibility is a common problem when capturing images in participating mediums such as mist or water. The problem of generating a haze-free image based on a hazy one can be described as image dehazing. Previous approaches dealt with this problem using physical models based on priors and simplifications. In this paper, we demonstrate that an end-to-end convolutional neural network is able to learn the dehazing process with no parameters or priors required, resulting in a more generic method. Even though our model is trained entirely with hazy indoor images, we are able to fully restore outdoor images with real haze. Also, we propose an architecture containing the novel Guided Layers, introduced in order to reduce the loss of spatial information while restoring the images. Our method outperforms other machine learning based models, yielding superior results both qualitatively and quantitatively.</abstract>
		<language>en</language>
		<tertiarytype>Full Paper</tertiarytype>
		<format>On-line</format>
		<size>7086 KiB</size>
		<numberoffiles>1</numberoffiles>
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		<e-mailaddress>lucasteixeirag11@gmail.com</e-mailaddress>
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