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Terahertz Super-Resolution Nondestructive Detection Algorithm Based on Edge Feature Convolution Network
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DOI：10.1109/ACCESS.2022.3184029.png)
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Abstract
En 中文
Much research has been conducted to improve the defect-detection rate and detection accuracy of the imaging technology used in terahertz nondestructive testing. Due to the power limit of light sources and noise interference in terahertz equipment, images have low resolution and fuzzy defect edges. Hence, improving the resolution is crucial for detecting defects. We designed an edge detection network structure based on a traditional deep neural network. Besides, we devised a node-fusing strategy to train the network. It demonstrates significant improvement of the resolution of the terahertz defect contour. A quartz fiber composites with embedded defects was tested with our network. The results showed that the proposed super-resolution reconstruction algorithm improves resolution, particularly on the edges of defect contours.
Keywords:
Terahertz wave imaging
Feature extraction
Convolution
Image edge detection
Training
Circuit breakers
Superresolution
Composite materials
deep neural network
nondestructive testing
super-resolution algorithm
terahertz imaging
Journal
IF:
3.6
Papers:
9.8W
Citations:
29.4W
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