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About Watergan: Unsupervised Generative Network To Enable Real-Time Color Correction of Monocular Underwater Images
This paper proposes WaterGAN, an unsupervised generative adversarial network (GAN) that generates realistic synthetic underwater images from input in-air images and depth maps. WaterGAN is trained on unlabeled real underwater images to learn environmental conditions at a survey site. Its synthetic output serves as training data for a two-stage color correction network that takes raw underwater images as input and outputs color-corrected images as if taken in air, without requiring real ground truth training data. The approach enables real-time color correction of monocular underwater images using deep learning without extensive data collection.
Detailed Information
| Author: | ['Wafa Benzaoui'] |
|---|---|
| Publication Year: | 2019 |
| Pages: | 8 |
| Language: | English |
| Format: | |
| Price: | FREE |
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