RT Journal Article
T1 Fusion-based variational image dehazing
A1 Galdran, Adrian
A1 Vazquez-Corral, Javier
A1 Pardo, David
A1 Bertalmio, Marcelo
AB We propose a novel image-dehazing technique based on the minimization of two energy functionals and a fusion scheme to combine the output of both optimizations. The proposed fusion-based variational image-dehazing (FVID) method is a spatially varying image enhancement process that first minimizes a previously proposed variational formulation that maximizes contrast and saturation on the hazy input. The iterates produced by this minimization are kept, and a second energy that shrinks faster intensity values of well-contrasted regions is minimized, allowing to generate a set of difference-of-saturation (DiffSat) maps by observing the shrinking rate. The iterates produced in the first minimization are then fused with these DiffSat maps to produce a haze-free version of the degraded input. The FVID method does not rely on a physical model from which to estimate a depth map, nor it needs a training stage on a database of human-labeled examples. Experimental results on a wide set of hazy images demonstrate that FVID better preserves the image structure on nearby regions that are less affected by fog, and it is successfully compared with other current methods in the task of removing haze degradation from faraway regions.
SN 1070-9908
YR 2017
FD 2017-02
LK https://hdl.handle.net/11556/4545
UL https://hdl.handle.net/11556/4545
LA eng
NO Galdran , A , Vazquez-Corral , J , Pardo , D & Bertalmio , M 2017 , ' Fusion-based variational image dehazing ' , IEEE Signal Processing Letters , vol. 24 , no. 2 , 7792620 , pp. 151-155 . https://doi.org/10.1109/LSP.2016.2643168
NO Publisher Copyright: © 1994-2012 IEEE.
DS TECNALIA Publications
RD 1 sept 2024