Call for Paper - July 2019 Edition
IJCA solicits original research papers for the July 2019 Edition. Last date of manuscript submission is June 20, 2019. Read More

Crack Detection and Restoration in Digitized Paintings by Using Top Hat Transform Method and Median Filter

Print
PDF
2nd National Conference on Information and Communication Technology
© 2011 by IJCA Journal
Number 1 - Article 3
Year of Publication: 2011
Authors:
Prateeksha Chouksey
Pratik Chouksey
Pranali Dandekar

Prateeksha Chouksey, Pratik Chouksey and Pranali Dandekar. Article: Crack Detection and Restoration in Digitized Paintings by using Top Hat Transform Method and Median Filter. IJCA Proceedings on 2nd National Conference on Information and Communication Technology NCICT(4):, November 2011. Full text available. BibTeX

@article{key:article,
	author = {Prateeksha Chouksey and Pratik Chouksey and Pranali Dandekar},
	title = {Article: Crack Detection and Restoration in Digitized Paintings by using Top Hat Transform Method and Median Filter},
	journal = {IJCA Proceedings on 2nd National Conference on Information and Communication Technology},
	year = {2011},
	volume = {NCICT},
	number = {4},
	pages = {},
	month = {November},
	note = {Full text available}
}

Abstract

Digital Restoration is an integrated methodology for the detection and removal of cracks from digitized paintings. The older paintings suffer from breaks in the substrate, the paint, or the varnish. When we digitized these paintings, they can be modified using mathematical algorithms and cracks are eliminated soas to maintain the quality. The cracks are detected by thresholding the output of the morphological top hat transform. Cracks usually have low luminance and, thus, can be considered as local intensity minima with rather elongated structural characteristics. A crack detector can be applied on the luminance component of an image to identify such minima. Afterwards, 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 such as median filter is performed. The methodology has been shown to perform very well on digitized paintings suffering from cracks, thereby ensuring its originality.

Reference

  • M. Barni, F. Bartolini, and V. Cappellini, “Image processing for virtual restoration of artworks,”IEEE Multim dia, vol. 7,no. 2,pp. 34–37,Jun. 2000.
  • F. Abas and K. Martinez, “Craquelure analysis for content - based retrieval,” in Proc. 14th Int. Conf. Digital Signal Processing, vol. 1, 2002, pp. 111–114.
  • L. Joyeux, O. Buisson, B. Besserer, and S. Boukir, “Detection and removal of line scratches in motion picture films,” in Proc. IEEE Int. Conf. Computer Vision and Pattern Recognition, 1999, pp. 548–553.
  • A. Kokaram, R. Morris, W. Fitzgerald, and P. Rayner, “Detection of missing data in image sequences,” IEEE Trans. Image Process., vol. 4, no. 11, pp. 1496–1508, Nov. 1995.
  • “Interpolation of missing data in image sequences,” IEEE Trans. Image Process., vol. 4, no. 11, pp. 1509–1519, Nov.1995.
  • M. Bertalmio, G. Sapiro, V. Caselles, and C. Ballester, “Image in painting,” in Proc. SIGGRAPH, 2000, pp. 417–424.
  • C. Ballester, M. Bertalmio, V. Caselles, G. Sapiro, and J. Verdera.