Digital image processing techniques for the detection and removal of cracks in digitized paintings.
An integrated methodology for the detection and removal of cracks on digitized paintings is presented in this paper. The cracks are detected by thresholding the output of the morphological top-hat transform. Afterward, the thin dark brush strokes which have been misidentified as cracks are removed using either a median radial basis function neural network on hue and saturation data or a semi-automatic procedure based on region growing. Finally, crack filling using order statistics filters or controlled anisotropic diffusion is performed. The methodology has been shown to perform very well on digitized paintings suffering from cracks.
Giakoumis
Ioannis
I
Department of Informatics, Aristotle University of Thessaloniki, 54124 Thessaloniki, Greece. giio@intranet.gr
Nikolaidis
Nikos
N
Pitas
Ioannis
I
D000465:Algorithms; D016477:Artifacts; D001185:Artificial Intelligence; D003196:Computer Graphics; D007089:Image Enhancement / Q000379:methods*; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D016247:Information Storage and Retrieval / Q000379:methods; D010151:Paintings; D010363:Pattern Recognition, Automated / Q000379:methods*; D012815:Signal Processing, Computer-Assisted