Abstract
In the field of non-destructive testing (NDT), low-cost uncooled infrared (IR) sensors cannot generate high-speed thermography detection results with low levels of noise and high sensitivity. Hence, this paper proposes a compressive sensing-based postprocessing method for the results of uncooled IR sensors to obtain high-quality and super-resolution NDT imaging results. Super-resolution IR images are generated using low-resolution IR images and sparse dictionaries generated from randomly sampled raw patches of training images. Super-resolution IR images and pulsed phase thermography (PPT) are combined to improve the NDT results for carbon fiber-reinforced polymer (CFRP) specimens with artificial defects engraved on them. The results of the PPT experiments show that the compressive sensing-based super-resolution algorithm can be used to double the resolution of PPT phase images. The reconstructed PPT phase images further show that the proposed method can produce sharp defect edges while preserving the original texture, which will enable the use of uncooled IR sensors in a wider range of NDT applications.
Published Version
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