Deep learning for dehazing: Comparison and analysis

A Benoit (1) , Leonel Cuevas (2) , Jean-Baptiste Thomas (3, 2)

1 LISTIC - Laboratoire d'Informatique, Systèmes, Traitement de l'Information et de la Connaissance
2 The Norwegian Colour and Visual Computing Laboratory
3 Le2i - Laboratoire Electronique, Informatique et Image [UMR6306]

We compare a recent dehazing method based on deep learning, Dehazenet, with traditional state-of-the-art approaches , on benchmark data with reference. Dehazenet estimates the depth map from transmission factor on a single color image, which is used to inverse the Koschmieder model of imaging in the presence of haze. In this sense, the solution is still attached to the Koschmieder model. We demonstrate that the transmission is very well estimated by the network, but also that this method exhibits the same limitation than others due to the use of the same imaging model.

Domaines: Traitement du signal et de l'image [eess.SP]; Intelligence artificielle [cs.AI]; Vision par ordinateur et reconnaissance de formes [cs.CV]; Multimédia [cs.MM]; Réseau de neurones [cs.NE]; Machine Learning [stat.ML]; Traitement du signal et de l'image [eess.SP]

A Benoit, Leonel Cuevas, Jean-Baptiste Thomas. Deep learning for dehazing: Comparison and analysis. Colour and Visual Computing Symposium (CVCS), Sep 2018, Gjøvik, Norway. ⟨hal-01823911⟩