Abstract
A model based approach to noise reduction in images is discussed in this paper. The images are modelled as Markov Random Fields (MRF). A MRF is characterized by an energy function, and several energy functions which are used in image processing are presented.
Given a noisy image and an image model it is possible to find the maximum a posterior solution within a Bayesian framework. This, however, requires a search among a very large number of image configurations. A (practical) way to solve the problem is to use the simulated annealing algorithm. Due to the lattice structure of an image it is possible to increase the speed of the annealing and some methods are discussed. Finally two examples are shown.
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© 1993 Springer-Verlag Berlin Heidelberg
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Olsson, C.K. (1993). Simulated Annealing in Image Processing. In: Vidal, R.V.V. (eds) Applied Simulated Annealing. Lecture Notes in Economics and Mathematical Systems, vol 396. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-46787-5_16
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DOI: https://doi.org/10.1007/978-3-642-46787-5_16
Publisher Name: Springer, Berlin, Heidelberg
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