RT Conference Proceedings
T1 A variational framework for single image Dehazing
A1 Galdran, Adrian
A1 Vazquez-Corral, Javier
A1 Pardo, David
A1 Bertalmío, Marcelo
A2 Rother, Carsten
A2 Agapito, Lourdes
A2 Bronstein, Michael M.
AB Images captured under adverse weather conditions, such as haze or fog, typically exhibit low contrast and faded colors, which may severely limit the visibility within the scene. Unveiling the image structure under the haze layer and recovering vivid colors out of a single image remains a challenging task, since the degradation is depth-dependent and conventional methods are unable to handle this problem. We propose to extend a well-known perception-inspired variational framework [1] for the task of single image dehazing. The main modification consists on the replacement of the value used by this framework for the grey-world hypothesis by an estimation of the mean of the clean image. This allows us to devise a variational method that requires no estimate of the depth structure of the scene, performing a spatially-variant contrast enhancement that effectively removes haze from far away regions. Experimental results show that our method competes well with other state-of-the-art methods in typical benchmark images, while outperforming current image dehazing methods in more challenging scenarios.
PB Springer Verlag
SN 9783319161983
SN 0302-9743
YR 2015
FD 2015
LK https://hdl.handle.net/11556/1620
UL https://hdl.handle.net/11556/1620
LA eng
NO Galdran , A , Vazquez-Corral , J , Pardo , D & Bertalmío , M 2015 , A variational framework for single image Dehazing . in C Rother , L Agapito & M M Bronstein (eds) , Computer Vision - ECCV 2014 Workshops, Proceedings . Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) , vol. 8927 , Springer Verlag , pp. 259-270 , 13th European Conference on Computer Vision, ECCV 2014 , Zurich , Switzerland , 6/09/14 . https://doi.org/10.1007/978-3-319-16199-0_18
NO conference
NO Publisher Copyright: © Springer International Publishing Switzerland 2015.
DS TECNALIA Publications
RD 1 sept 2024