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As a result, common edge detection and threshold segmentation techniques fail to identify these kinds of defects. In this work, we present a robust algorithm for defect identification in magnetic tile images. The proposed method is based on a new anisotropic diffusion filtering model. Unlike traditional anisotropic diffusion models that take into account only gradient magnitude information, the proposed model combines together gradient magnitude and a new local difference image feature. The aim is to remove bright shapes and undesirable artifacts in the faultless region in magnetic tile images. In addition, the method activates a smoothing process in the flawless region to homogenize the background and simultaneously a sharpening in the defect boundaries to highlight anomalies. Experimental results on a number of magnetic tiles samples containing different types of defects have demonstrated the efficiency of the proposed diffusion method.<\/jats:p>","DOI":"10.1177\/0142331220982220","type":"journal-article","created":{"date-parts":[[2021,1,25]],"date-time":"2021-01-25T21:24:50Z","timestamp":1611609890000},"page":"2413-2424","update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":12,"title":["Defect identification in magnetic tile images using an improved nonlinear diffusion method"],"prefix":"10.1177","volume":"43","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-1687-8542","authenticated-orcid":false,"given":"Mohamed","family":"Ben Gharsallah","sequence":"first","affiliation":[{"name":"National Higher School of Engineering (ENSIT), Research CEREP Unit, Tunisia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ezzedine","family":"Ben 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