| Autor: |
Sappa, Angel D., Suárez, Patricia L., Velesaca, Henry O., Carpio, Darío |
| Předmět: |
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| Zdroj: |
International Conference Computer Graphics, Visualization, Computer Vision & Image Processing; 2022, p85-92, 8p |
| Abstrakt: |
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. [ABSTRACT FROM AUTHOR] |
| Databáze: |
Complementary Index |
| Externí odkaz: |
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