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Abstract This study investigated a dictionary-learning (DL)-based image deblurring method 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 tube with a focal spot size of 0.6 mm and a flat-panel detector with a pixel size of 100 μ m2. X-ray images of several electronic components were acquired at x-ray tube conditions of 80 kV p and 1.25 mAs. The image characteristics of the deblurred images generated by the DL-based algorithm were quantitatively evaluated in terms of intensity profile, universal-quality index, and noise power spectrum. Our results indicate that our DL-based image deblurring method effectively improves image performance in x-ray nondestructive testing. Highlights • A dictionary-learning-based image deblurring method was investigated. • A computational simulation and experiment were performed. • The image characteristics were quantitatively evaluated. • The proposed method effectively improves image performance in x-ray NDT. [ABSTRACT FROM AUTHOR]

In conventional radiography, image visibility is often limited mainly due to the superimposition of the object’s structure under investigation and scattered x-rays. Several methods, including the antiscatter grid technique, the air-gap technique, and scatter correction methods using measurements, mathematical-physical modeling, or a combination of both, have been extensively investigated in an attempt to overcome these difficulties. However, these methods require special equipment, geometry, and extra work to measure the scatter characteristics. In this study, we propose a new image restoration method based on a simple radiographic scattering model in which the intensity of the scattered x-rays and the direct transmission function of a given object are estimated from a single x-ray image by using the dark-channel prior. We implemented the proposed algorithm and performed a systematic experiment by using a 450-kV industrial x-ray inspection system to demonstrate its viability for nondestructive testing. Our results indicated that the structure of the examined object was much more clearly visible in the restored image, considerably improving the radiographic visibility. [ABSTRACT FROM AUTHOR]

We have recently developed precise x-ray grids having strip densities in the range of 100 – 250 lines/inch by adopting the precision sawing process and carbon interspace material for the demands of specific x-ray imaging techniques. However, quality assurance in the grid manufacturing has not yet satisfactorily conducted because grid strips of a high strip density are often invisible through an x-ray nondestructive testing with a flat-panel detector of an ordinary pixel resolution (>100 μ m). In this work, we propose a useful method to evaluate actual grid strip densities over the Nyquist sampling rate based on the moiré artifact analysis. We performed a systematic simulation and experiment with several sample grids and a detector having a 143- μ m pixel resolution to verify the proposed quality assurance method. According to our results, the relative differences between the nominal and the evaluated grid strip densities were within 0.2% and 1.8% in the simulation and experiment, respectively, which demonstrates that the proposed method is viable with an ordinary detector having a moderate pixel resolution for quality assurance in grid manufacturing. [ABSTRACT FROM AUTHOR]