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Delving into Underwater Image Utility: Benchmark Dataset and Prediction Model.

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Title: Delving into Underwater Image Utility: Benchmark Dataset and Prediction Model.
Authors: Liu, Jiapeng1 (AUTHOR), Liu, Yi2 (AUTHOR), Jiang, Qiuping1 (AUTHOR) jiangqiuping@nbu.edu.cn
Source: Remote Sensing. Jun2025, Vol. 17 Issue 11, p1906. 20p.
Subjects: Convolutional neural networks, Image intensifiers, Computer vision, Source code, Prediction models
Abstract: High-quality underwater images are essential for both human visual perception and machine analysis in marine vision applications. Although significant progress has been achieved in Underwater Image Quality Assessment (UIQA), almost all existing UIQA methods focus on the visual perception-oriented image quality issue and cannot be used to gauge the utility of underwater images for the use in machine vision applications. To address this issue, in this work, we focus on the problem of automatic underwater image utility assessment (UIUA). On the one hand, we first construct a large-scale Object Detection-oriented Underwater Image Utility Assessment (OD-UIUA) dataset, which includes 1200 raw underwater images, corresponding to 12,000 enhanced results by 10 representative underwater image enhancement (UIE) algorithms and 13,200 underwater image utility scores (UIUSs) for all raw and enhanced underwater images in the dataset. On the other hand, based on this newly constructed OD-UIUA dataset, we train a deep UIUA network (DeepUIUA) that can automatically and accurately predict UIUS. To the best of our knowledge, this is the first benchmark dataset for UIUA and also the first model focusing on the specific UIUA problem. We comprehensively compare the performance of our proposed DeepUIUA model with that of 14 state-of-the-art no-reference image quality assessment (NR-IQA) methods by using the OD-UIUA dataset as the benchmark. Extensive experiments showcase that our proposed DeepUIUA model has superior performance compared with the existing NR-IQA methods in assessing UIUS. The OD-UIUA dataset and the source code of our DeepUIUA model will be released. [ABSTRACT FROM AUTHOR]
Copyright of Remote Sensing is the property of MDPI and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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  Data: Delving into Underwater Image Utility: Benchmark Dataset and Prediction Model.
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  Data: High-quality underwater images are essential for both human visual perception and machine analysis in marine vision applications. Although significant progress has been achieved in Underwater Image Quality Assessment (UIQA), almost all existing UIQA methods focus on the visual perception-oriented image quality issue and cannot be used to gauge the utility of underwater images for the use in machine vision applications. To address this issue, in this work, we focus on the problem of automatic underwater image utility assessment (UIUA). On the one hand, we first construct a large-scale Object Detection-oriented Underwater Image Utility Assessment (OD-UIUA) dataset, which includes 1200 raw underwater images, corresponding to 12,000 enhanced results by 10 representative underwater image enhancement (UIE) algorithms and 13,200 underwater image utility scores (UIUSs) for all raw and enhanced underwater images in the dataset. On the other hand, based on this newly constructed OD-UIUA dataset, we train a deep UIUA network (DeepUIUA) that can automatically and accurately predict UIUS. To the best of our knowledge, this is the first benchmark dataset for UIUA and also the first model focusing on the specific UIUA problem. We comprehensively compare the performance of our proposed DeepUIUA model with that of 14 state-of-the-art no-reference image quality assessment (NR-IQA) methods by using the OD-UIUA dataset as the benchmark. Extensive experiments showcase that our proposed DeepUIUA model has superior performance compared with the existing NR-IQA methods in assessing UIUS. The OD-UIUA dataset and the source code of our DeepUIUA model will be released. [ABSTRACT FROM AUTHOR]
– Name: AbstractSuppliedCopyright
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  Data: <i>Copyright of Remote Sensing is the property of MDPI and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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        Value: 10.3390/rs17111906
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      – Code: eng
        Text: English
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        PageCount: 20
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      – SubjectFull: Convolutional neural networks
        Type: general
      – SubjectFull: Image intensifiers
        Type: general
      – SubjectFull: Computer vision
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      – SubjectFull: Source code
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      – SubjectFull: Prediction models
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      – TitleFull: Delving into Underwater Image Utility: Benchmark Dataset and Prediction Model.
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            NameFull: Liu, Jiapeng
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            NameFull: Jiang, Qiuping
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            – D: 01
              M: 06
              Text: Jun2025
              Type: published
              Y: 2025
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