{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,6]],"date-time":"2026-04-06T15:04:47Z","timestamp":1775487887589,"version":"3.50.1"},"reference-count":50,"publisher":"Association for Computing Machinery (ACM)","issue":"2","license":[{"start":{"date-parts":[[2015,3,5]],"date-time":"2015-03-05T00:00:00Z","timestamp":1425513600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"funder":[{"name":"Swiss National Science Foundation &num;200021-130261"}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["J. Comput. Cult. Herit."],"published-print":{"date-parts":[[2015,3,5]]},"abstract":"<jats:p>\n            Today\u2019s information society needs efficient and economic solutions for the digital restoration of the photographic heritage. Different methods have been adopted up to now for the automatic detection of dust and scratches; each method has pros and cons, and a limited field of effectiveness. The use of infrared radiation and the spatiotemporal image analysis are among the most effective methods, although they have their limits. The infrared radiation only works for dye-based material, while the spatiotemporal image analysis is not applicable for still images and is limited due to motion in the scene. The present work defines in detail a set of methods for optical dust and scratches detection applicable on any type of transparent photographic material (silver-based as well as dye-based material, still images as well as moving images). The term \u201coptical\u201d refers to the fact that the considered methods seek physical evidence of the presence of foreign bodies or irregularities on the film; this allows avoiding the typical digital artifacts produced by \u201cnonoptical\u201d methods, for which certain elements of the scenes are erroneously obliterated because they resemble dust grains or scratches. \u201cPDD\u201d (Polarized Dark-field Detection) detects the flaws using an image acquired in a polarized dark-field setup; \u201cDCD\u201d (Dual Collimation Detection) takes advantage of the Callier effect to locate the flaws; \u201c\n            <jats:italic>n<\/jats:italic>\n            -MDD\u201d (Multiple Direction Detection) entails the acquisition of\n            <jats:italic>n<\/jats:italic>\n            images in dark-field setups with different directions of illumination, and the extraction of the differences between the images through multivariate analysis. A numerical evaluation of the performances of the MDD method with an eightfold acquisition (8-MDD) is carried out by comparing its flaw detection with the flaw detection provided by commercial software based on spatiotemporal image analysis.\n          <\/jats:p>","DOI":"10.1145\/2597894","type":"journal-article","created":{"date-parts":[[2015,3,9]],"date-time":"2015-03-09T19:03:01Z","timestamp":1425927781000},"page":"1-19","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":6,"title":["Optical Detection of Dust and Scratches on Photographic Film"],"prefix":"10.1145","volume":"8","author":[{"given":"Giorgio","family":"Trumpy","sequence":"first","affiliation":[{"name":"Digital Humanities Lab - Universit\u00e4t Basel"}]},{"given":"Rudolf","family":"Gschwind","sequence":"additional","affiliation":[{"name":"Digital Humanities Lab - Universit\u00e4t Basel"}]}],"member":"320","published-online":{"date-parts":[[2015,3,5]]},"reference":[{"key":"e_1_2_2_1_1","unstructured":"J. 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