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Using total variation denoising for detecting defects in industrial radiography

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Radiography is often the method of choice for detecting defects and damage in industrial objects. To optimise the operator's image perception and interpretation, and to improve the detection limit of the technique, the signal-to-noise ratio needs to be minimised. The development of effective image noise removal (denoising) algorithms, with highfrequency spatial signal retention (fine detail features), is an important research area in industrial radiographic testing (RT). In this study, the total variation denoising (TVD) method was used to improve RT defect detection capabilities. The method relies on generating a regularised smoothed image, which is then subtracted from the original image to reconstruct the denoised image. The algorithm was successfully applied to weld RT images. Improved defect detection was achieved whilst preserving the object's edge and fine detail imaging information. For the imaged samples in this study, true positive detection rates of between 88% and 100% were found in the samples for the different defects in TVD-reconstructed images, compared with between 84% and 100% in the original. The results show that the TVDreconstructed images have better contrast and the shapes of the defects are very clear.
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Keywords: DEFECTS; IMAGE PROCESSING; RADIOGRAPHY; TOTAL VARIATION DENOISING; WELD

Document Type: Research Article

Publication date: April 1, 2016

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