Dictionary-learning-based image deblurring for improving image performance in x-ray nondestructive testing
2019
Abstract This study investigated a dictionary-learning (DL)-based image
deblurringmethod for improving image performance in x-ray
nondestructive testing. DL is a representation learning theory that aims to find a sparse representation of the input signal in the form of a linear combination of basic elements as well as those basic elements themselves. In this study, a DL-based algorithm was implemented, and a computational simulation and experiment were then performed to evaluate the algorithm’s effectiveness for image
deblurring. The hardware system used in the experiment consisted of an
x-ray tubewith a focal spot size of 0.6 mm and a
flat-panel detectorwith a pixel size of 100 μ m 2 . X-ray images of several electronic components were acquired at
x-ray tubeconditions of 80 kV p and 1.25 mAs. The image characteristics of the
deblurredimages generated by the DL-based algorithm were quantitatively evaluated in terms of intensity profile, universal-quality index, and
noise powerspectrum. Our results indicate that our DL-based image
deblurringmethod effectively improves image performance in x-ray
nondestructive testing.
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