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April 1977 (vol. 26 no. 4)
pp. 351-364
S.J. Wernecke, Department of Electrical Engineering, Stanford University
Two-dimensional digital image reconstruction is an important imaging process in many of the physical sciences. If the data are insufficient to specify a unique reconstruction, an additional criterion must be introduced, either implicitly or explicitly before the best estimate can be computed. Here we use a principle of maximum entropy, which has proven useful in other contexts, to design a procedure for reconstruction from noisy measurements. Implementation is described in detail for the Fourier synthesis problem of radio astronomy. The method is iterative and hence more costly than direct techniques; however, a number of comparative examples indicate that a significant improvement in image quality and resolution is possible with only a few iterations. A major component of the computational burden of the maximum entropy procedure is shown to be a two-dimensional convolution sum, which can be efficiently calculated by fast Fourier transform techniques.
Index Terms:
Digital image processing, Fourier synthesis, image processing, image reconstruction, maximum entropy, radio telescopes, statistical estimation theory.
S.J. Wernecke, L.R. D'Addario, "Maximum Entropy Image Reconstruction," IEEE Transactions on Computers, vol. 26, no. 4, pp. 351-364, April 1977, doi:10.1109/TC.1977.1674845
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