Reconstruction of Gray-scale Images

Details Reconstruction of Gray-scale Images Reconstruction of Gray-scale Images

2000-03-16

arxiv.org/abs/math/0003098v2

Methodol. Comput. Appl. Probab. 3 (2001), no. 3, 255--270 (2002)

Mathematics

Statistics Theory

18 pages

Scientific paper

10.1023/A:1013762722096

Reconstruction of images corrupted by noise is an important problem in Image Analysis. In the standard Bayesian approach the unknown original image is assumed to be a realization of a Markov random field on a finite two dimensional finite region. This image is degraded by some noise, which acts independently in each site of and has the same distribution on all sites. The reconstructed image is the maximum a posteriori (MAP) estimator. When the number of colors is bigger than two the standard a priori measure is the Potts model, but with this choice reconstruction algorithms need exponential growing time in the number of pixels. We propose a different a priori measure that allows to compute the MAP estimator in polinomial time. The algorithm binary decomposes the intensity of each color and reconstructs each component, reducing the problem to the computation of the two-color MAP estimator. The latter is done in polinomial time solving a min-cut max-flow problem in a binary graph as proposed by Greig, Porteous and Seheult (1989).

Affiliated with

Also associated with

No associations

Rating

Reconstruction of Gray-scale Images does not yet have a rating. At this time, there are no reviews or comments for this scientific paper.

If you have personal experience with Reconstruction of Gray-scale Images, we encourage you to share that experience with our LandOfFree.com community. Your opinion is very important and Reconstruction of Gray-scale Images will most certainly appreciate the feedback.

Rate now

Comments { 0 }

Profile ID: LFWR-SCP-O-551917