ITERATIVE MULTIFRAME SUPERRESOLUTION ALGORITHMS FOR ATMOSPHERIC-TURBULENCE-DEGRADED IMAGERY

The subject of interest is the superresolution of atmospheric-turbulen ce-degraded, short-exposure imagery, where superresolution refers to t he removal of blur caused by a diffraction-limited optical system alon g with recovery of some object spatial-frequency components outside th e optical passband. Photon-limited space object images are of particul ar interest. Two strategies based on multiple exposures are explored. The first is known as deconvolution from wave-front sensing, where est imates of the optical transfer function (OTF) associated with each exp osure are derived from wave-front-sensor data. New multiframe superres olution algorithms are presented that. are based on Bayesian maximum a posteriori and maximum-likelihood formulations. The second strategy i s known as blind deconvolution, in which the OTF associated with each frame is unknown and must be estimated, A new multiframe blind deconvo lution algorithm is presented that is based on a Bayesian maximum-like lihood formulation with strict constraints incorporated by using nonli near reparameterizations. Quantitative simulation of imaging through a tmospheric turbulence and wave-front sensing are used to demonstrate t he superresolution performance of the algorithms. (C) 1998 Optical Soc iety of America.