gion) and to manage complicated cases where the de-
graded area intersects a darker region of the image
as in Fig. 5, so that restoration does not create arti-
facts in correspondence to not degraded pixels. This
is a great advantage, since the detection mask heavily
influences restoration results of available restoration
frameworks.
It is also worth highlighting that even though the
proposed algorithm involves iterative procedures, it
uses simple and fast operations and 4/5 iterations on
average to converge.
Future research will be oriented to refine the pro-
posed model to make it more flexible and adaptive to
different amount of degradation while faithfully pre-
serving original image information.
REFERENCES
Bertalmio, M., Sapiro, G., Caselles, V., and Bellester, B.
(2000). Image inpainting. In Proceedings of SIG-
GRAPH 2000.
Bertalmio, M., Vese, L., Sapiro, G., and Osher, S. (2003).
Simultaneous structure and texture image inpainting.
In IEEE Transactions on Image Processing, 12(8).
Bruni, V., Crawford, A., Kokaram, A., and Vitulano, D.
(2011). Semi-transparent blotches removal from sepia
images exploiting visibility laws. In Signal Image and
Video Processing. Springer-Verlag.
Crawford, A., Bruni, V., Kokaram, A., and Vitulano, D.
(2007). Multiscale semitransparent blotch removal
on archived photographs using bayesian matting tech-
niques and visibility laws. In Proceedings of In-
ternational Conference on Image Processing (ICIP
2007),S. Antonio, Florida. IEEE Signal Processing
Society.
Criminisi, A., Perez, P., and Toyama, K. (2004). Region
filling and object removal by exemplar-based image
inpainting. In IEEE Transactions on Image Process-
ing, 13(9). IEEE Signal Processing Society.
Drummond, T. and Cipolla, R. (2000). Application of lie
algebras to visual servoing. In International Journal
of Computer Vision, 37(1).
Efros, A. and Freeman, W. (2001). Image quilting for tex-
ture synthesis and transfer. In Proceedings of SIG-
GRAPH 2001.
Greenblatt, A., Agaian, S., and Panetta, K. (2008). Restora-
tion of images damaged by semi-transparent water
blotches using localized image enhancement. In Pro-
ceedings of SPIE 2008.
Helgason, S. (1962). Differential geometry and symmet-
ric spaces. Pure and Applied Mathematics, Vol. XII.,
Academic Press, New York-London.
Kokaram, A. (1998). Motion Picture Restoration. Digital
Algorithms for Artefact Suppression in Degraded Mo-
tion Picture Film and Video. Springer Verlag.
Kokaram, A. (2002). Parametric texture synthesis for fill-
ing holes in pictures. In Proceedings of International
Conference on Image Processing (ICIP 2002). IEEE
Signal Processing Society.
Mansouri, A. and Mukherjee, D. (2004). Constraining ac-
tive contour evolution via lie groups of transforma-
tion. In IEEE Transactions on Image Processing,
13(6). IEEE Signal Processing Society.
Mukherjee, D. and Acton, S. (2007). Affine and projective
active contour models. In Pattern Recognition, 40(3).
Elsevier Science Inc.
Nilsson, M., Dahl, M., and Claesson, I. (2005). The succes-
sive mean quantization transform. In Proceedings of
ICASSP. IEEE Signal Processing Society.
Pappas, T., Safranek, R., and Chen, J. (2005). Perceptual
criteria for image quality evaluation. In Handbook of
Image and Video Processing. Academic Press.
Porikli, F., Tuzel, O., and Meer, P. (2006). Covariance track-
ing using model update based on lie algebra. In Pro-
ceedings of the IEEE Conference on Computer Vision
and Pattern Recognition (CVPR 2006).
Stanco, F., Tenze, L., and Ramponi, G. (2005). Virtual
restoration of vintage photographic prints affected by
foxing and water blotches. In Journal of Electronic
Imaging, 14(4).
Varadarajan, V. (1974). Lie groups, Lie algebras and their
representations. Prentice-Hall Series in Modern Anal-
ysis. Prentice-Hall.
Winkler, S. (2005). Digital Video Quality. Vision Models
and Metrics. Wiley.
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