Deep learning for dehazing: Comparison and analysis
This is an incremental analysis for image processing researchers, highlighting model constraints in dehazing applications.
The paper compares a deep learning dehazing method, Dehazenet, with traditional approaches on benchmark data, showing it accurately estimates transmission but shares limitations due to reliance on the Koschmieder model.
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.