Image Recovery by Simulated Annealing
Final rept. Aug 1989-Dec 1989,
HARRY DIAMOND LABS ADELPHI MD
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The basic problem of image recovery and pattern recognition is to determine the original pattern, f, given its corrupted version, g. The unknown pattern f is an element of the set S f1, f2, f3, and the task is to deduce which pattern in S gave rise to the image data, g. S is called the solution candidate space and could be, for example, the set of alphabetical symbols. If it is known that certain elements of S have a higher probability of occurring than others such as alphabetical symbols in text, this a priori information can be incorporated into the procedure for finding f according to the techniques of Bayesian analysis. In the general image recovery problem, S is the set of all possible patterns on an n x n pixel image, and the relationship between the original image, f, and the image data, g, can be modeled by g f 2, where w is random noise. Set of pixel brightnesses at lattice position i,j are described by f fij, g gij, and w wij. Image recovery and pattern recognition problems are thus combinatorial optimization problems, in which a solution candidate space, S, must be searched. The larger S is, the more difficult the search.