DAUT: Underwater Image Enhancement Using Depth Aware U-shape Transformer

Images captured underwater are subject to different complex effects including absorption and scattering. Recovery of the original images is not a trivial task. The underwater image formation varies according to many dependencies. Also, underwater image enhancement models have been affected by the la...

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Bibliographic Details
Published in2023 IEEE International Conference on Image Processing (ICIP) pp. 1830 - 1834
Main Authors Badran, Mohamed, Torki, Marwan
Format Conference Proceeding
LanguageEnglish
Published IEEE 08.10.2023
Subjects
Absorption
Codes
Estimation
Generative Adversarial Network
Measurement
Scattering
Training
Transformer
Transformers
Underwater Image Dataset
Underwater Image Enhancement
Underwater Vision
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Summary:Images captured underwater are subject to different complex effects including absorption and scattering. Recovery of the original images is not a trivial task. The underwater image formation varies according to many dependencies. Also, underwater image enhancement models have been affected by the lack of large-scale underwater datasets with reference-enhanced images. Therefore, in this paper, we use cycleGAN to generate a new underwater dataset from existing in-air datasets. We train our data with a U-shape Transformer, which is one of the state-of-the-art models. We add a depth estimation module as object depth is one of the most vital dependencies to the underwater image formation model. The estimated depth and the underwater image are the input to our depth-aware U-transformer. Our experiments show that our model achieves state-of-the-art results in objective and subjective evaluations compared with the original U-shape Transformer and other state-of-the-art methods. the code and dataset are available at https://github.com/MBadran2000/Depth-Aware-U-shape-Transformer.git
DOI:10.1109/ICIP49359.2023.10222895