Digital Restoration of Archaeological Heritage
American Journal of Applied Mathematics
Volume 3, Issue 1-2, January 2015, Pages: 9-13
Received: Nov. 14, 2014; Accepted: Dec. 6, 2014; Published: Dec. 19, 2014
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Supriya P. Deshpande, Dept of CSE, PESIT, South Campus, Bangalore, India-560100
Preeti Sangamesh, Asst.Professor, Dept. of CSE, PESIT-South Campus, Bangalore, India – 560100
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Virtual Restoration of archaeological heritage stems from the need to create a clearer and better image of the beautiful historic monuments now in ruins. It gives the viewer a sense and feel of how the heritage originally looked. For this, there are many restoration projects of the major historic sites, paintings etc, going on across the world. Computers have been introduced to archaeology and cultural heritage as tools for promoting scientific work and as electronic aids for providing users with substantial information on archaeological heritage. Small holes and breakages in the monuments/paintings can severely degrade its appeal to viewers. Image restoration is the operation of taking a corrupted/noisy image and estimating the clean original image. In this work, we have proposed a method to automatically detect the defect in the corrupted image using Perona Malik Anisotropic Diffusion and Binary Thresholding, followed by Image Inpainting with Navier-Stokes Method, which have been found to be effective in the art of restoring lost/selected parts of an image based on the background information in a visually plausible way.
Automatic Detection of Defect, Image Inpainting, Image Restoration, Navier-Stokes Inpainting
To cite this article
Supriya P. Deshpande, Preeti Sangamesh, Digital Restoration of Archaeological Heritage, American Journal of Applied Mathematics. Special Issue: Frontiers in Mathematics and Computing. Vol. 3, No. 1-2, 2015, pp. 9-13. doi: 10.11648/j.ajam.s.2015030102.12
Gilles Aubert, Pierre Kornprobst, “Mathematical Problems in Image Processing Partial Differential Equations and the Calculus of Variations”
P. P. A. Criminisi, K. Toyama, “Region filling and object removal by exemplar-based inpainting", in: IEEE Trans. Image Processing, vol. 13, no. 9, 2004, pp. 1200-1212.
Atelea, Alexandru. “An image inpainting technique based on the fast marching method.” Journal of graphics tools 9.1 (2004): 23-34.
Bertalmio, Marcelo, Andrea L. Bertozzi, and Guillermo Sapiro. “Navier-stokes, fluid dynamics, and image and video inpainting.” In Computer Vision and Pattern Recognition, 2001. CVPR 2001. Proceedings of the 2001 IEEE Computer Society Conference on, vol. 1, pp. I-355. IEEE, 2001.
Pietro Perona and Jitendra Malik (July 1990). "Scale-space and edge detection using anisotropic diffusion". IEEE Transactions on Pattern Analysis and Machine Intelligence, 12 (7): 629–639
Joachim Weickert, “Anisotropic Diffusion in Image Processing”.
Canny, J., A “Computational Approach To Edge Detection”, IEEE Trans. Pattern Analysis and Machine Intelligence
M. Bertalmio, G. Sapiro, C. Ballester and V. Caselles, “Image inpainting,” Computer Graphics, SIGGRAPH 2000, pp. 417-424, July 2000.
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