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Related Experiment Video

Updated: Jul 5, 2026

Crack Monitoring in Resonance Fatigue Testing of Welded Specimens Using Digital Image Correlation
05:30

Crack Monitoring in Resonance Fatigue Testing of Welded Specimens Using Digital Image Correlation

Published on: September 29, 2019

A hybrid underwater crack image enhancement method.

Dongyan Ding1, Xinnan Fan2, Pengfei Shi3

  • 1College of Network and Telecommunications Engineering, JinLing Institute of Technology, Nanjing, 211169, Jiangsu, China. ddy@jit.edu.cn.

Scientific Reports
|July 3, 2026
PubMed
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This study introduces a hybrid method for underwater crack image processing, combining traditional techniques with deep learning (DL) for enhanced clarity. The novel approach improves image quality metrics, outperforming existing methods for underwater crack detection.

Area of Science:

  • Computer Vision
  • Image Processing
  • Artificial Intelligence

Background:

  • Underwater crack detection is crucial for infrastructure integrity.
  • Traditional image processing methods struggle with underwater image degradation.
  • Deep learning (DL) offers powerful feature extraction but can lack interpretability.

Purpose of the Study:

  • To develop a hybrid image processing method for underwater crack detection.
  • To enhance the clarity and quality of underwater crack images.
  • To combine the strengths of traditional image processing and DL.

Main Methods:

  • A hybrid algorithm integrating traditional image analysis (chromaticity factor, background light estimation) and deep learning (CNNs with edge feature extraction).
  • Utilized depthwise separable convolution and pixel attention mechanisms within CNNs.
Keywords:
Convolution neural networkDeep learningTraditional image processingUnderwater crack image enhancement

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Last Updated: Jul 5, 2026

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  • Fused image features with edge features for improved crack representation.

    • Main Results:

    • The hybrid method demonstrated superior performance in enhancing underwater crack images.
    • Achieved significant improvements in Peak Signal-to-Noise Ratio (PSNR) and Structural Similarity Index Measure (SSIM).
    • Outperformed existing traditional and DL-based image enhancement algorithms.

    Conclusions:

    • The proposed hybrid algorithm effectively enhances underwater crack images by combining traditional interpretability with DL generality.
    • Offers a robust solution for underwater crack image restoration and analysis.
    • Provides a foundation for improved structural health monitoring in underwater environments.