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Physics-inspired enhancement framework for industrial digital radiography based on radiation-matter interaction
Fayu Chen1, Guancheng Lu2, Wei Wei3
1School of Computer, Electronics and Information, Guangxi University, Nanning, 530004, China.
Scientific Reports
|July 9, 2026
Summary
This study introduces a new physics-inspired framework for industrial digital radiography, enhancing defect visibility in X-ray images by separating scattering. The method improves image quality for nondestructive evaluation without needing annotations.
Area of Science:
- Physics
- Image Processing
- Materials Science
Background:
- Industrial digital radiography images suffer from scattering, obscuring defects and challenging conventional enhancement techniques.
- Existing methods often compromise noise suppression or detail preservation when addressing scattering.
- A novel, annotation-free approach is needed to accurately separate scattering and improve image fidelity.
Purpose of the Study:
- To develop a physics-inspired, annotation-free framework for industrial digital radiography image enhancement.
- To explicitly separate the scattering component from detected intensity using a radiation-matter interaction model.
- To improve defect visibility and structural fidelity in industrial nondestructive evaluation.
Main Methods:
- A multistage pipeline involving attenuation estimation, adaptive scatter modeling, and residual scatter removal.
- Incorporation of edge sharpening and local contrast enhancement to recover the direct transmission signal.
- Evaluation on industrial weld radiographs from ship plates, boilers, and oil pipelines.
Main Results:
- The proposed method outperformed global histogram equalization, CLAHE, DWT, and a CNN baseline in enhancing image quality.
- Achieved the highest average contrast-to-noise ratio (up to 2.94) and lowest image quality evaluator scores (NIQE: 4.67, BRISQUE: 22.37).
- Ablation studies confirmed the contribution of each pipeline stage, and sensitivity analyses demonstrated stable performance.
Conclusions:
- The developed framework effectively separates scattering and enhances details in industrial X-ray images, improving defect detection.
- It offers a significant advancement for nondestructive evaluation, providing better structural fidelity and defect visibility.
- The C# source code is available, facilitating further research and application in industrial settings.
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