Underwater Image Enhancement Using Pre-trained Transformer

Abderrahmene Boudiaf; Yuhang Guo; Adarsh Ghimire; Naoufel Werghi; Giulia De Masi; Sajid Javed; Jorge Dias

doi:10.1007/978-3-031-06433-3_41

Underwater Image Enhancement Using Pre-trained Transformer

Abderrahmene Boudiaf
, Yuhang Guo
, Adarsh Ghimire
, Naoufel Werghi
, Giulia De Masi
, Sajid Javed
, Jorge Dias

Electrical Engineering

ARRC

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

19 Scopus citations

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.

Original language	British English
Title of host publication	Image Analysis and Processing – ICIAP 2022 - 21st International Conference, 2022, Proceedings
Editors	Stan Sclaroff, Cosimo Distante, Marco Leo, Giovanni M. Farinella, Federico Tombari
Publisher	Springer Science and Business Media Deutschland GmbH
Pages	480-488
Number of pages	9
ISBN (Print)	9783031064326
DOIs	https://doi.org/10.1007/978-3-031-06433-3_41
State	Published - 2022
Event	21st International Conference on Image Analysis and Processing, ICIAP 2022 - Lecce, Italy Duration: 23 May 2022 → 27 May 2022

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	13233 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	21st International Conference on Image Analysis and Processing, ICIAP 2022
Country/Territory	Italy
City	Lecce
Period	23/05/22 → 27/05/22

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 14 Life Below Water

Keywords

Image enhancement
Underwater imaging
Vision transformer

Access to Document

10.1007/978-3-031-06433-3_41

OpenUrl availability

Full text

Cite this

Boudiaf, A., Guo, Y., Ghimire, A., Werghi, N., De Masi, G., Javed, S., & Dias, J. (2022). Underwater Image Enhancement Using Pre-trained Transformer. In S. Sclaroff, C. Distante, M. Leo, G. M. Farinella, & F. Tombari (Eds.), Image Analysis and Processing – ICIAP 2022 - 21st International Conference, 2022, Proceedings (pp. 480-488). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 13233 LNCS). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-3-031-06433-3_41

Boudiaf, Abderrahmene ; Guo, Yuhang ; Ghimire, Adarsh et al. / Underwater Image Enhancement Using Pre-trained Transformer. Image Analysis and Processing – ICIAP 2022 - 21st International Conference, 2022, Proceedings. editor / Stan Sclaroff ; Cosimo Distante ; Marco Leo ; Giovanni M. Farinella ; Federico Tombari. Springer Science and Business Media Deutschland GmbH, 2022. pp. 480-488 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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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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author = "Abderrahmene Boudiaf and Yuhang Guo and Adarsh Ghimire and Naoufel Werghi and \{De Masi\}, Giulia and Sajid Javed and Jorge Dias",

note = "Funding Information: 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. Publisher Copyright: {\textcopyright} 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.; 21st International Conference on Image Analysis and Processing, ICIAP 2022 ; Conference date: 23-05-2022 Through 27-05-2022",

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Boudiaf, A, Guo, Y, Ghimire, A, Werghi, N, De Masi, G, Javed, S & Dias, J 2022, Underwater Image Enhancement Using Pre-trained Transformer. in S Sclaroff, C Distante, M Leo, GM Farinella & F Tombari (eds), Image Analysis and Processing – ICIAP 2022 - 21st International Conference, 2022, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 13233 LNCS, Springer Science and Business Media Deutschland GmbH, pp. 480-488, 21st International Conference on Image Analysis and Processing, ICIAP 2022, Lecce, Italy, 23/05/22. https://doi.org/10.1007/978-3-031-06433-3_41

Underwater Image Enhancement Using Pre-trained Transformer. / Boudiaf, Abderrahmene; Guo, Yuhang; Ghimire, Adarsh et al.
Image Analysis and Processing – ICIAP 2022 - 21st International Conference, 2022, Proceedings. ed. / Stan Sclaroff; Cosimo Distante; Marco Leo; Giovanni M. Farinella; Federico Tombari. Springer Science and Business Media Deutschland GmbH, 2022. p. 480-488 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 13233 LNCS).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Underwater Image Enhancement Using Pre-trained Transformer

AU - Boudiaf, Abderrahmene

AU - Guo, Yuhang

AU - Ghimire, Adarsh

AU - Werghi, Naoufel

AU - De Masi, Giulia

AU - Javed, Sajid

AU - Dias, Jorge

N1 - Funding Information: 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. Publisher Copyright: © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

PY - 2022

Y1 - 2022

N2 - 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.

AB - 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.

KW - Image enhancement

KW - Underwater imaging

KW - Vision transformer

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U2 - 10.1007/978-3-031-06433-3_41

DO - 10.1007/978-3-031-06433-3_41

M3 - Conference contribution

AN - SCOPUS:85131150387

SN - 9783031064326

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

SP - 480

EP - 488

BT - Image Analysis and Processing – ICIAP 2022 - 21st International Conference, 2022, Proceedings

A2 - Sclaroff, Stan

A2 - Distante, Cosimo

A2 - Leo, Marco

A2 - Farinella, Giovanni M.

A2 - Tombari, Federico

PB - Springer Science and Business Media Deutschland GmbH

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Y2 - 23 May 2022 through 27 May 2022

ER -

Boudiaf A, Guo Y, Ghimire A, Werghi N, De Masi G, Javed S et al. Underwater Image Enhancement Using Pre-trained Transformer. In Sclaroff S, Distante C, Leo M, Farinella GM, Tombari F, editors, Image Analysis and Processing – ICIAP 2022 - 21st International Conference, 2022, Proceedings. Springer Science and Business Media Deutschland GmbH. 2022. p. 480-488. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-031-06433-3_41

Underwater Image Enhancement Using Pre-trained Transformer

Abstract

Publication series

Conference

UN SDGs

Keywords

Access to Document

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