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Related Experiment Video

Updated: Jan 17, 2026

Reefshape: A System for the Efficient Collection and Automated Processing of Time-Series Underwater Photogrammetry Data for Benthic Habitat Monitoring
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Underwater image enhancement using colour balancing and morphological residual processing through gamma correction.

Dawa Chyophel Lepcha1, Bhawna Goyal2,3, Ayush Dogra4

  • 1Graduate Institute of Biomedical Informatics, College of Medical Science and Technology, Taipei Medical University, New Taipei, 235, Taiwan.

Scientific Reports
|January 14, 2026
PubMed
Summary

This study introduces an efficient underwater image enhancement framework that restores natural colors and improves visibility without training data. The method significantly outperforms existing techniques for marine and robotic imaging applications.

Keywords:
Color balancingGamma correctionGrey-world approachMorphological residual processingUnderwater image enhancementWeight maps

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Area of Science:

  • Marine Biology and Ecology
  • Robotics and Computer Vision
  • Image Processing

Background:

  • Underwater images suffer from poor visibility, low contrast, and color distortion due to light absorption and scattering.
  • These degradations hinder visual quality and impact marine and robotic imaging applications.
  • Existing underwater image enhancement (UIE) methods often require training data or depth estimation, limiting their real-time applicability.

Purpose of the Study:

  • To develop an efficient and robust underwater image enhancement framework for natural color restoration and structural improvement.
  • To provide a training-free and depth-independent solution for real-time UIE applications.
  • To enhance the quality of underwater imagery for improved marine monitoring and inspection.

Main Methods:

  • An adaptive color compensation strategy corrects channel imbalances.
  • Morphological residual processing refines textures and suppresses noise.
  • Adaptive multiscale fusion and gamma correction ensure balanced contrast, brightness, and detail preservation.

Main Results:

  • The proposed UIE framework achieves superior performance compared to 22 state-of-the-art methods.
  • Quantitative assessments show significant improvements in Peak Signal-to-Noise Ratio (PSNR), Structural Similarity Index Measure (SSIM), Underwater Image Quality Measure (UIQM), and Underwater Color Image Quality Evaluation (UCIQE).
  • The method effectively restores realistic colors, enhances visibility, and preserves fine details in underwater images.

Conclusions:

  • The developed framework offers an effective, lightweight, and computationally efficient solution for practical underwater image enhancement.
  • The method's robustness and real-time capability make it suitable for diverse marine and robotic applications.
  • This work supports Sustainable Development Goals 14, 9, and 12 through enhanced underwater monitoring and inspection.