research-article

A deep Retinex network for underwater low-light image enhancement

Authors:

Wei ZhangAuthors Info & Claims

Machine Vision and Applications, Volume 34, Issue 6

https://doi.org/10.1007/s00138-023-01478-z

Published: 16 October 2023 Publication History

Abstract

Underwater images suffer from color cast and low contrast due to the light absorption and scattering. Especially when natural light is not sufficient, large dark areas appear in the captured image, making it impossible to understand the image content. To address this issue, we propose an underwater low-light enhancement method based on Retinex theory. Our model is an end-to-end trainable. The decomposition network decomposes the raw image into reflectance and illumination according to Retinex theory. In the reflectance enhancement network, cross-residual blocks and dense connections can improve the efficiency of feature utilization and the hybrid attention concentrate on the regions of interest in feature maps from different perspectives. The illumination adjustment network utilizes adaptive frequency convolutional blocks to generate additional band information, which reconstructs the more natural illumination. In order to preserve the color consistency of the enhanced image with the reference image, we project the HSV space into the Cartesian coordinate system and use the Euclidean distance as the color cast loss to constrain the enhancement network. Qualitative and quantitative evaluations on different underwater datasets indicate that our method has the excellent performance and can achieve delightful visual enhancements.

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Cited By

Liu JWang HWang JHe DXu RTang X(2024)Thermal infrared action recognition with two-stream shift Graph Convolutional NetworkMachine Vision and Applications10.1007/s00138-024-01550-235:4Online publication date: 13-May-2024
https://dl.acm.org/doi/10.1007/s00138-024-01550-2
Boisclair JAmamou AKelouwani SAlam MOueslati HZeghmi LAgbossou K(2024)A tree-based approach for visible and thermal sensor fusion in winter autonomous drivingMachine Vision and Applications10.1007/s00138-024-01546-y35:4Online publication date: 3-May-2024
https://dl.acm.org/doi/10.1007/s00138-024-01546-y

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Information

Published In

cover image Machine Vision and Applications

Machine Vision and Applications Volume 34, Issue 6

Nov 2023

561 pages

ISSN:0932-8092

Issue’s Table of Contents

© The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

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Springer-Verlag

Berlin, Heidelberg

Publication History

Published: 16 October 2023

Accepted: 25 September 2023

Revision received: 20 August 2023

Received: 09 May 2023

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Fundamental Research Funds for the Central Universities
’Jie Bang Gua Shuai’ Science and Technology Major Project of Liaoning Province in 2022
National Key Research and Development Program of China

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Cited By

Liu JWang HWang JHe DXu RTang X(2024)Thermal infrared action recognition with two-stream shift Graph Convolutional NetworkMachine Vision and Applications10.1007/s00138-024-01550-235:4Online publication date: 13-May-2024
https://dl.acm.org/doi/10.1007/s00138-024-01550-2
Boisclair JAmamou AKelouwani SAlam MOueslati HZeghmi LAgbossou K(2024)A tree-based approach for visible and thermal sensor fusion in winter autonomous drivingMachine Vision and Applications10.1007/s00138-024-01546-y35:4Online publication date: 3-May-2024
https://dl.acm.org/doi/10.1007/s00138-024-01546-y

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