PT Unknown
AU Angel Sappa
   Patricia Suarez
   Henry Velesaca
   Dario Carpio
TI Domain Adaptation in Image Dehazing: Exploring the Usage of Images from Virtual Scenarios
BT 16th International Conference on Computer Graphics, Visualization, Computer Vision and Image Processing
PY 2022
BP 85
EP 92
DE Domain adaptation; Synthetic hazed dataset; Dehazing
AB This work presents a novel domain adaptation strategy for deep learning-based approaches to solve the image dehazing problem. Firstly, a large set of synthetic images is generated by using a realistic 3D graphic simulator; these synthetic images contain different densities of haze, which are used for training the model that is later adapted to any real scenario.The adaptation process requires just a few images to fine-tune the model parameters. The proposed strategy allows overcoming the limitation of training a given model with few images. In other words, the proposed strategy implements the adaptation of a haze removal model trained with synthetic images to real scenarios. It should be noticed that it is quite difficult, if not impossible, to have large sets of pairs of real-world images (with and without haze) to train in a supervised way dehazing algorithms. Experimental results are provided showing the validity of the proposed domain adaptation strategy.
ER