Abstract

We present efficient algorithms for image restoration by means of Good’s roughness penalty. We assume Gaussian or Poisson statistics for the noise and derive an algorithm for each case. Performance is tested by simulated three-dimensional imaging with a fluorescence confocal laser scanning microscope. Results are compared with those for algorithms that use Gaussian or entropy penalty terms, which we derived previously [J. Opt. Soc. Am. A 14, 1696 (1997)]. The algorithms based on Good’s roughness yield superior results. An example is given of the restoration of an image of a biological specimen.

© 1998 Optical Society of America

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