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January 1978 (vol. 27 no. 1)
pp. 32-36
M. Kanefsky, Department of Electrical Engineering, University of Pittsburgh
This paper considers a detection theory approach to the restoration of digitized images. The images are modeled as second-order Markov meshes. This model is not only well suited to a decision approach to smoothing, but it enables computer simulations of images thereby permitting a statistical analysis of restoration techniques. Smoothing procedures that are near optimal in the sense of approaching a nonrealizable bound are demonstrated and evaluated. The achievable reduction in mean-square error is considerable for coarsely quantized pictures. This reduction, for the four-level pictures considered, is somewhat greater than that achievable by linear techniques. The approach actually minimizes the probability of error which may be important for preserving picture features.
Index Terms:
image simulations, Decision theory, image enhancement
Citation:
M. Kanefsky, M.G. Strintzis, "A Decision Theory Approach to Picture Smoothing," IEEE Transactions on Computers, vol. 27, no. 1, pp. 32-36, Jan. 1978, doi:10.1109/TC.1978.1674949
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