Abstract

I describe implementations of the Two-Mu image-restoration algorithm that model the center portion of the convolution of the point-spread function and the original image (this has been done heretofore), as well as those that model the full range of that convolution. The full convolution methods produce processed images of simple, simulated scenes that are comparable in quality with, and often involve computations that are considerably shorter than, those of the center convolution methods. The full convolution methods incur some loss of information near the edge of the scene. However, that loss may not be significant for large images, especially for those in which the important information is far from the edge of the scene.

© 2001 Optical Society of America

Fast maximum-likelihood image-restoration algorithms for three- dimensional fluorescence microscopy

Joanne Markham and José-Angel Conchello
J. Opt. Soc. Am. A 18(5) 1062-1071 (2001)

Stochastic artificial retinas: algorithm, optoelectronic circuits, and implementation

Philippe Lalanne, Donald Prévost, and Pierre Chavel
Appl. Opt. 40(23) 3861-3876 (2001)

Improved implementation algorithms of the two-dimensional nonseparable linear canonical transform

Jian-Jiun Ding, Soo-Chang Pei, and Chun-Lin Liu
J. Opt. Soc. Am. A 29(8) 1615-1624 (2012)

Anisoplanatic imaging through turbulent media: image recovery by local information fusion from a set of short-exposure images

Mikhail A. Vorontsov and Gary W. Carhart
J. Opt. Soc. Am. A 18(6) 1312-1324 (2001)

Human- and model-observer performance in ramp-spectrum noise: effects of regularization and object variability

Craig K. Abbey and Harrison H. Barrett
J. Opt. Soc. Am. A 18(3) 473-488 (2001)