Statistics – Computation
Scientific paper
Jan 1995
adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=1995phdt.........6y&link_type=abstract
Thesis (PH.D.)--THE UNIVERSITY OF ARIZONA, 1995.Source: Dissertation Abstracts International, Volume: 56-10, Section: B, page: 5
Statistics
Computation
Maximum A-Posteriori
Scientific paper
In this dissertation, the super-resolution method that we use for image restoration is the Poisson Maximum A-Posteriori (MAP) super-resolution algorithm of Hunt, computed with an iterative form. This algorithm is similar to the Maximum Likelihood of Holmes, which is derived from an Expectation/Maximization (EM) computation. Image restoration of point source data is our focus. This is because most astronomical data can be regarded as multiple point source data with a very dark background. The statistical limits imposed by photon noise on the resolution obtained by our algorithm are investigated. We improve the performance of the super-resolution algorithm by including the additional information of the spatial constraints. This is achieved by applying the well-known CLEAN algorithm, which is widely used in astronomy, to create regions of support for the potential point sources. Real and simulated data are included in this paper. The point spread function (psf) of a diffraction limited optical system is used for the simulated data. The real data is two dimensional optical image data from the Hubble Space Telescope.
Yuen Patrick Wingkee
No associations
LandOfFree
Applying Modified Clean Algorithm to Map Image Super-Resolution does not yet have a rating. At this time, there are no reviews or comments for this scientific paper.
If you have personal experience with Applying Modified Clean Algorithm to Map Image Super-Resolution, we encourage you to share that experience with our LandOfFree.com community. Your opinion is very important and Applying Modified Clean Algorithm to Map Image Super-Resolution will most certainly appreciate the feedback.
Profile ID: LFWR-SCP-O-837176