Abstract
The goal of this work is to apply a denoising image transformer to remove the distortion from underwater images and compare it with other similar approaches. Automatic restoration of underwater images plays an important role since it allows to increase the quality of the images, without the need for more expensive equipment. This is a critical example of the important role of the machine learning algorithms to support marine exploration and monitoring, reducing the need for human intervention like the manual processing of the images, thus saving time, effort, and cost. This paper is the first application of the image transformer-based approach called “Pre-Trained Image Processing Transformer” to underwater images. This approach is tested on the UFO-120 dataset, containing 1500 images with the corresponding clean images.
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Acknowledgement
This work acknowledges the support provided by the Khalifa University of Science and Technology under awards No. RC1-2018-KUCARS, and CIRA-2019-047. The second author, Sajid Javed, of this publication is supported by the FSU-2022-003 Project under Award No. 000628-00001.
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Boudiaf, A. et al. (2022). Underwater Image Enhancement Using Pre-trained Transformer. In: Sclaroff, S., Distante, C., Leo, M., Farinella, G.M., Tombari, F. (eds) Image Analysis and Processing – ICIAP 2022. ICIAP 2022. Lecture Notes in Computer Science, vol 13233. Springer, Cham. https://doi.org/10.1007/978-3-031-06433-3_41
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