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%0 Conference Proceedings
%4 sid.inpe.br/sibgrapi/2017/08.21.00.34
%2 sid.inpe.br/sibgrapi/2017/08.21.00.34.08
%T DeepDive: An End-to-End Dehazing Method Using Deep Learning
%D 2017
%A Goncalves, Lucas Teixeira,
%A Gaya, Joel de Oliveira,
%A Drews-Jr, Paulo,
%A Botelho, Silvia Silva da Costa,
%@affiliation Universidade Federal do Rio Grande
%@affiliation Universidade Federal do Rio Grande
%@affiliation Universidade Federal do Rio Grande
%@affiliation Universidade Federal do Rio Grande
%E Torchelsen, Rafael Piccin,
%E Nascimento, Erickson Rangel do,
%E Panozzo, Daniele,
%E Liu, Zicheng,
%E Farias, Mylène,
%E Viera, Thales,
%E Sacht, Leonardo,
%E Ferreira, Nivan,
%E Comba, João Luiz Dihl,
%E Hirata, Nina,
%E Schiavon Porto, Marcelo,
%E Vital, Creto,
%E Pagot, Christian Azambuja,
%E Petronetto, Fabiano,
%E Clua, Esteban,
%E Cardeal, Flávio,
%B Conference on Graphics, Patterns and Images, 30 (SIBGRAPI)
%C Niterói, RJ
%8 Oct. 17-20, 2017
%S Proceedings
%I IEEE Computer Society
%J Los Alamitos
%K Deep Learning, Image Dehazing, Convolutional Neural Network.
%X Image dehazing can be described as the problem of mapping from a hazy image to a haze-free image. Most approaches to this problem use physical models based on simplifications and priors. In this work we demonstrate that a convolutional neural network with a deep architecture and a large image database is able to learn the entire process of dehazing, without the need to adjust parameters, resulting in a much more generic method. We evaluate our approach applying it to real scenes corrupted by haze. The results show that even though our network is trained with simulated indoor images, it is capable of dehazing real outdoor scenes, learning to treat the degradation effect itself, not to reconstruct the scene behind it.
%@language en
%3 PID4958913.pdf


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