X-radiography inspection techniques are generally used in non-destructive evaluationindustry. Manual assessment of the inspection may fail and turn into false assessment dueto a large number of examining while inspection process. Accordingly, incorrectassessment through human vision may cause immense industry disaster. Therefore, it isessential to examine through machine vision, which is capable to avoid false assessment.Digital X-radiography has widely been used in Non-Destructive Testing (NDT) andparticularly in weld defect detection. Weld defects occurrence is an unavoidable problemduring the welding process. Most common weld defects some of which are porosity, gaspore, tungsten inclusion, longitudinal crack, lack of penetration, and slag inclusion.Digital image processing techniques are a foremost way to experiment in the NDT. Thedetection and classification of weld defects depend on the quality of the digitized image, which is subjected to certain factors, such as noise, the mode of the image histogram, defects of different dimensions, indiscernible defects in the image background, and lowcontrast or unevenly illuminated image. More accuracy can be achieved during classificationof weld defects are always be subject to the deliverables of low and mid-level imageprocessing techniques. Therefore, it is desirable to provide more importance for these levelsin the weld X-radiography image.