%0 Conference Proceedings
%T Domain Adaptation in Image Dehazing: Exploring the Usage of Images from Virtual Scenarios
%A Angel Sappa
%A Patricia Suarez
%A Henry Velesaca
%A Dario Carpio
%B 16th International Conference on Computer Graphics, Visualization, Computer Vision and Image Processing
%D 2022
%F Angel Sappa2022
%O MSIAU; no proj
%O exported from refbase (http://158.109.8.37/show.php?record=3804), last updated on Tue, 25 Apr 2023 10:10:55 +0200
%X 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.
%K Domain adaptation
%K Synthetic hazed dataset
%K Dehazing
%U https://www.iadisportal.org/digital-library/iadis-international-conference-big-data-analytics-data-mining-and-computational-intelligence-2022-part-of-mccsis-2022
%U http://158.109.8.37/files/SSV2022.pdf
%P 85-92