@InProceedings{AngelSappa2022,
author="Angel Sappa
and Patricia Suarez
and Henry Velesaca
and Dario Carpio",
title="Domain Adaptation in Image Dehazing: Exploring the Usage of Images from Virtual Scenarios",
booktitle="16th International Conference on Computer Graphics, Visualization, Computer Vision and Image Processing",
year="2022",
pages="85--92",
optkeywords="Domain adaptation",
optkeywords="Synthetic hazed dataset",
optkeywords="Dehazing",
abstract="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.",
optnote="MSIAU; no proj",
optnote="exported from refbase (http://158.109.8.37/show.php?record=3804), last updated on Tue, 25 Apr 2023 10:10:55 +0200",
opturl="https://www.iadisportal.org/digital-library/iadis-international-conference-big-data-analytics-data-mining-and-computational-intelligence-2022-part-of-mccsis-2022",
file=":http://158.109.8.37/files/SSV2022.pdf:PDF"
}