28 October 1994 Fast restoration of atmospherically blurred images
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This paper concerns solving deconvolution problems for atmospherically blurred images by the preconditioned conjugate gradient algorithm, where a new approximate inverse preconditioner is used to increase the rate of convergence. Removing a linear, shift-invariant blur from a signal or image can be accomplished by inverse or Wiener filtering, or by an iterative least squares deblurring procedure. Because of the ill-posed characteristics of the deconvolution problem, in the presence of noise, filtering methods often yield poor results. On the other hand, iterative methods often suffer from slow convergence at high spatial frequencies. Theoretical results are established to show that fast convergence for our iterative algorithm can be expected, and test results are reported for a ground-based astronomical imaging problem.
© (1994) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
James G. Nagy, James G. Nagy, Robert J. Plemmons, Robert J. Plemmons, Todd C. Torgersen, Todd C. Torgersen, "Fast restoration of atmospherically blurred images", Proc. SPIE 2296, Advanced Signal Processing: Algorithms, Architectures, and Implementations V, (28 October 1994); doi: 10.1117/12.190866; https://doi.org/10.1117/12.190866

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