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9 November 1993 Noniterative methods for image deconvolution
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Three new algorithms for deconvolving image blur are presented. All three are based on the computation of the zeros of an image's z-transform and the separation of the zeros into sets belonging to the image and to the point spread function (psf). The zeros lie on a sheet existing in a four-dimensional space. The first two algorithms are applicable when the psf is known a priori. In Algorithm I portions of the 'zero sheet' are matched using a Euclidean measure, then zeros are selected from the remainder and an image is algebraically reconstructed. In Algorithm II the point zeros of 1-dimensional cuts through Fourier space are matched before reconstructing an image estimate via inverse Fourier transformation. Finally, Algorithm III is applicable when an ensemble of differently blurred images are recorded from the same object (e.g. astronomical speckle images); even through the psf is unknown for each member of the ensemble (i.e. deconvolution is to be 'blind'), parts of the zero sheet corresponding to the actual (unblurred) image can be matched over the ensemble and a reconstruction made by inverse Fourier transformation. Encouraging results have been obtained for Algorithms I and III for small positive images contaminated by small amounts of noise; Algorithm II has been successfully applied to larger images. Algorithms I and III have an inherent advantage over conventional Wiener filtering in that the psf does not need to be known precisely to achieve acceptable results.
© (1993) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Philip J. Bones, B. L. Satherley, C. R. Parker, and Russell W. Watson "Noniterative methods for image deconvolution", Proc. SPIE 2029, Digital Image Recovery and Synthesis II, (9 November 1993);


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