Title:	DOMAIN ADAPTATION IN IMAGE DEHAZING: EXPLORING THE USAGE OF IMAGES FROM VIRTUAL SCENARIOS
Author(s):	Angel D. Sappa, Patricia L. Suárez, Henry O. Velesaca and Darío Carpio
ISBN:	978-989-8704-42-9
Editors:	Yingcai Xiao, Ajith Abraham, Guo Chao Peng and Jörg Roth
Year:	2022
Edition:	Single
Keywords:	Domain Adaptation, Synthetic Hazed Dataset, Dehazing
Type:	Full Paper
First Page:	85
Last Page:	92
Language:	English
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Paper Abstract:	This work presents a novel domain adaptation strategy for deep learning-based approaches to solve the image haze removal 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 haze removal algorithms. Experimental results are provided showing the validity of the proposed domain adaptation strategy.
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