Fast Underwater Image Enhancement for Improved Visual Perception

In this letter, we present a conditional generative adversarial network-based model for real-time underwater image enhancement. To supervise the adversarial training, we formulate an objective function that evaluates the perceptual image quality based on its global content, color, local texture, and...

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Vydáno v:IEEE robotics and automation letters Ročník 5; číslo 2; s. 3226 - 3233
Hlavní autoři: Islam, Md Jahidul, Xia, Youya, Sattar, Junaed
Médium: Journal Article
Jazyk:angličtina
Vydáno: Piscataway IEEE 01.04.2020
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Témata:
computer vision for automation
deep learning in robotics and automation
Gallium nitride
Generators
Image detection
Image enhancement
Image quality
Marine robotics
Object recognition
Optical distortion
Performance enhancement
Real time
Real-time systems
Training
Underwater robots
Visibility
Visual perception
ISSN:2377-3766, 2377-3766
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Shrnutí:In this letter, we present a conditional generative adversarial network-based model for real-time underwater image enhancement. To supervise the adversarial training, we formulate an objective function that evaluates the perceptual image quality based on its global content, color, local texture, and style information. We also present EUVP, a large-scale dataset of a paired and an unpaired collection of underwater images (of 'poor' and 'good' quality) that are captured using seven different cameras over various visibility conditions during oceanic explorations and human-robot collaborative experiments. In addition, we perform several qualitative and quantitative evaluations which suggest that the proposed model can learn to enhance underwater image quality from both paired and unpaired training. More importantly, the enhanced images provide improved performances of standard models for underwater object detection, human pose estimation, and saliency prediction. These results validate that it is suitable for real-time preprocessing in the autonomy pipeline by visually-guided underwater robots. The model and associated training pipelines are available at https://github.com/xahidbuffon/funie-gan .
Bibliografie:ObjectType-Article-1
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ISSN:2377-3766
2377-3766
DOI:10.1109/LRA.2020.2974710