{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,25]],"date-time":"2026-06-25T17:14:36Z","timestamp":1782407676591,"version":"3.54.5"},"reference-count":37,"publisher":"MDPI AG","issue":"18","license":[{"start":{"date-parts":[[2020,9,9]],"date-time":"2020-09-09T00:00:00Z","timestamp":1599609600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Aiming at the influence of uneven illumination on fabric feature extraction and the limitations of traditional frequency-based visual saliency algorithms, we propose a fabric defect detection method based on the combination of illumination correction and visual salient features\u2014(1) Construct a multi-scale side window box (MS-BOX) filter to extract the illumination component of the image, then use the constructed two-dimensional gamma correction function to perform illumination correction on the image in the global angle, and finally enhance the local contrast of the image in the local angle; (2) Use the L0 gradient minimization method to remove the background texture of fabric images and highlight the defects; (3) Represent the fabric image as a quaternion image, where each pixel in the image is represented by a quaternion consisting of color, intensity and edge characteristics. The two-dimensional fractional Fourier transform (2D-FRFT) is used to obtain the saliency map of the quaternion image. Experiments show that our method has a higher overall recall rate for defect detection of star-patterned, box-patterned, and dot-patterned fabrics, and the overall recall-precision effect is better than other existing methods.<\/jats:p>","DOI":"10.3390\/s20185147","type":"journal-article","created":{"date-parts":[[2020,9,9]],"date-time":"2020-09-09T09:01:09Z","timestamp":1599642069000},"page":"5147","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":13,"title":["Fabric Defect Detection Based on Illumination Correction and Visual Salient Features"],"prefix":"10.3390","volume":"20","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-7071-4899","authenticated-orcid":false,"given":"Lan","family":"Di","sequence":"first","affiliation":[{"name":"School of Artificial Intelligence and Computer, Jiangnan University, Wuxi 214122, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2782-3248","authenticated-orcid":false,"given":"Hanbin","family":"Long","sequence":"additional","affiliation":[{"name":"School of Artificial Intelligence and Computer, Jiangnan University, Wuxi 214122, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3511-3374","authenticated-orcid":false,"given":"Jiuzhen","family":"Liang","sequence":"additional","affiliation":[{"name":"School of Information and Engineering, Changzhou University, Changzhou 213164, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2020,9,9]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"123","DOI":"10.1177\/004051759506500301","article-title":"Applying an Artificial Neural Network to Pattern Recognition in Fabric Defects","volume":"65","author":"Tsai","year":"1995","journal-title":"Text. 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