Paper
27 February 2007 Dust and scratch removal in scanned images
Ruth Bergman, Hila Nachlieli, Gitit Ruckenstein, Darryl Greig
Author Affiliations +
Proceedings Volume 6497, Image Processing: Algorithms and Systems V; 649709 (2007) https://doi.org/10.1117/12.703403
Event: Electronic Imaging 2007, 2007, San Jose, CA, United States
Abstract
Dust, scratches or hair on originals (prints, slides or negatives) distinctly appear as light or dark artifacts on a scan. These unsightly artifacts have become a major consumer concern. This paper describes an algorithmic solution to the dust and scratch removal task. The solution is divided into two phases: a detection phase and a reconstruction phase. Some scanners have dedicated hardware to detect dust and scratch areas in the original. Without hardware assistance, dust and scratch removal algorithms generally resort to blurring, at the loss of image detail. We present an algorithmic alternative for dust and scratch detection that effectively differentiates between defects and image details. In addition we present reconstruction algorithms, that preserve image sharpness better than available alternatives. For detection we generate a detail-less image in which the defects are "erased". We compare properties of the luminance channel of the input image relative to the detailless image. For reconstruction of the defective areas we suggest both a fast small support algorithm and a large support algorithm, which is better able to mimic the existing image texture.
© (2007) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Ruth Bergman, Hila Nachlieli, Gitit Ruckenstein, and Darryl Greig "Dust and scratch removal in scanned images", Proc. SPIE 6497, Image Processing: Algorithms and Systems V, 649709 (27 February 2007); https://doi.org/10.1117/12.703403
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CITATIONS
Cited by 2 scholarly publications and 1 patent.
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KEYWORDS
Reconstruction algorithms

Detection and tracking algorithms

Defect detection

Scanners

Algorithm development

Image quality

Eye

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