Model-driven terahertz image reconstruction method for debonding defects in thermal barrier coatings

Area of Science:

• Non-destructive testing
• Materials science
• Image processing

Background:

• Terahertz imaging is crucial for detecting defects in gas turbine engine thermal barrier coatings.
• Existing terahertz imaging systems suffer from slow acquisition, low resolution, and edge blur, limiting their practical application.

Purpose of the Study:

• To develop an advanced model-driven terahertz image reconstruction method.
• To enhance the resolution, edge definition, and overall quality of terahertz images for defect analysis.

Main Methods:

• A model-driven approach using simulation data to create comprehensive datasets.
• Development of a fusion loss function incorporating edge intensity for improved edge reconstruction.
• Comparative analysis against Bicubic, SRCNN, and VDSR methods.

Main Results:

• The proposed method significantly improves visual quality and evaluation indices for reduced terahertz images.
• Effective restoration of defect contours and sharpening of image edges were achieved.
• Demonstrated superior performance compared to existing super-resolution techniques.

Conclusions:

• The developed method offers a robust solution for enhancing terahertz image quality in defect detection.
• It effectively addresses limitations of conventional terahertz imaging systems.
• The technique holds significant industrial value for gas turbine engine maintenance and inspection.