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Adaptive fusion based deep learning framework for restoring underwater image quality using multi scale attention

T Veeramakali1, Md Shohel Sayeed1, Sumendra Yogarayan2

  • 1Centre for Intelligent Cloud Computing, COE for Advanced Cloud, Faculty of Information Science and Technology, Multimedia University, Jalan Ayer Keroh Lama, Bukit Beruang, Malaka, 75450, Malaysia.

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Summary

This study introduces an Efficient Restoration of Underwater Images Using Multi-Scale Attention Features (ERUI-MSAF) model. The ERUI-MSAF model effectively enhances underwater image visibility and quality, outperforming existing methods.

Keywords:
Adaptive bilateral filteringDeep learningMulti-scale attention featuresRestorationUnderwater images

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Area of Science:

  • Computer Vision
  • Image Processing
  • Deep Learning

Background:

  • Underwater images suffer from degradation like blurring, low contrast, and color deviation.
  • Restoring underwater images is crucial for various practical applications.
  • Traditional methods struggle with complex underwater image degradations.

Purpose of the Study:

  • To develop an effective method for restoring underwater images.
  • To improve visibility and overall quality of underwater images.
  • To introduce the Efficient Restoration of Underwater Images Using Multi-Scale Attention Features (ERUI-MSAF) model.

Main Methods:

  • Adaptive Bilateral Filtering (ABF) for noise reduction and pre-processing.
  • ERUI-MSAF model integrating channel and spatial attention features.
  • Fusion of Deep Wavenet (DWN) for spatial features and EfficientNet for channel features.

Main Results:

  • The ERUI-MSAF model adaptively emphasizes informative features and regions.
  • Achieved superior Peak Signal-to-Noise Ratio (PSNR) values of 34.258 and 29.0073 on EUVP and UIEB datasets.
  • Demonstrated high performance and computational efficiency compared to existing models.

Conclusions:

  • The proposed ERUI-MSAF model is effective for underwater image restoration.
  • The integration of multi-scale attention features significantly improves image quality.
  • The method offers a promising solution for enhancing underwater imagery.