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Updated: Jul 16, 2026

Pore-scale Imaging and Characterization of Hydrocarbon Reservoir Rock Wettability at Subsurface Conditions Using X-ray Microtomography

Pore-scale Imaging and Characterization of Hydrocarbon Reservoir Rock Wettability at Subsurface Conditions Using X-ray Microtomography

Published on: October 21, 2018

X-Ray Weld Image Detection Method of Water Injection Network Based on Sparse Representation.

Hailong Liu^1,2, Weixin Gao^1,2, Li Gao^1,2

¹School of Electronic Engineering, Xi'an Shiyou University, Xi'an 710065, China.

Sensors (Basel, Switzerland)

|July 15, 2026

Summary

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X-ray Imaging

X-ray Imaging

German physicist Wilhelm Röntgen (1845–1923) was experimenting with electrical current when he discovered that a mysterious and invisible "ray" would pass through his flesh but leave an outline of his bones on a screen coated with a metal compound. In 1895, Röntgen made the first durable record of the internal parts of a living human: an "X-ray" image (as it came to be called) of his wife’s hand. Scientists worldwide quickly began their own experiments with X-rays, and by 1900, X-ray was widely...

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This study introduces a novel sparse representation framework for detecting minute defects in X-ray weld testing. The method enhances image quality and accurately identifies small flaws like cracks and pinholes.

Area of Science:

Materials Science
Non-Destructive Testing (NDT)
Image Processing

Background:

X-ray testing is crucial for weld integrity assessment.
Minute defects (cracks, pinholes) are challenging to detect due to their small size and noise interference.
Existing NDT methods struggle with subtle defect identification.

Purpose of the Study:

To develop an advanced framework for recognizing minute defects in weld X-ray images.
To improve the accuracy and reliability of non-destructive testing for critical weld flaws.
To address the limitations of current techniques in identifying small, low-contrast defects.

Main Methods:

Implemented median filtering and image enhancement to improve feature discriminability.
Developed a segmented region of interest (ROI) extraction using Otsu thresholding and Sobel edge detection for adaptable weld analysis.

Keywords:

X-ray weld image defect detection sparse representation water injection network

Related Experiment Videos

Last Updated: Jul 16, 2026

Pore-scale Imaging and Characterization of Hydrocarbon Reservoir Rock Wettability at Subsurface Conditions Using X-ray Microtomography

Pore-scale Imaging and Characterization of Hydrocarbon Reservoir Rock Wettability at Subsurface Conditions Using X-ray Microtomography

Published on: October 21, 2018

Utilized a sparse representation framework with dictionary learning and sparse solving models for micro-defect classification.

Main Results:

The proposed method effectively enhanced the detection of minute weld defects.
The segmented ROI extraction method demonstrated robustness for inclined and curved weld images.
Sparse representation achieved accurate classification of micro-defect regions, outperforming traditional approaches.

Conclusions:

The developed framework offers a significant advancement in non-destructive testing for weld integrity.
The approach provides a valuable tool for engineering applications requiring precise defect detection.
Validation through real-world data and tests confirms the method's effectiveness and practical utility.