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| Author | Angel Sappa; Patricia Suarez; Henry Velesaca; Dario Carpio | ||||
| Title | Domain Adaptation in Image Dehazing: Exploring the Usage of Images from Virtual Scenarios | Type | Conference Article | ||
| Year | 2022 | Publication | 16th International Conference on Computer Graphics, Visualization, Computer Vision and Image Processing | Abbreviated Journal | |
| Volume | Issue | Pages | 85-92 | ||
| Keywords | Domain adaptation; Synthetic hazed dataset; 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. |
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| Address | Lisboa; Portugal; July 2022 | ||||
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| Area | Expedition | Conference | CGVCVIP | ||
| Notes | MSIAU; no proj | Approved | no | ||
| Call Number | Admin @ si @ SSV2022 | Serial | 3804 | ||
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