Abstract
Weld quality inspection using industrial radiography is considered to be one of the most important processes in critical industries such as aeronautical manufacturing. The quality of radiographic images of welded industrial parts may suffer from poor signal-to-noise ratio (SNR), the main cause of which is the unavoidable detection of scattered X-rays. Image processing methods may be used to enhance image contrast and achieve improved defect detection. In this study, the outcomes from three different image contrast enhancement spatial domain transform algorithms are analysed and compared. The three algorithms used are normalised convolution (NC), interpolated convolution (IC) and recursive filtering (RF). Based on the results of qualitative operator perception, the study shows that the application of all three methods results in improved image contrast, enabling enhanced visualisation of image detail. Subtle differences in performance between the outputs from the different algorithms are noted, especially around the edges of image features. Furthermore, it is found that RF is approximately two orders of magnitude quicker than the other algorithms, making it more suitable for online weld inspection lines.
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