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.