Constraining information for improvements to MAP image super-resolution

Patrick Wingkee Yuen; Bobby R. Hunt

doi:10.1117/12.188036

30 September 1994 Constraining information for improvements to MAP image super-resolution

Patrick Wingkee Yuen, Bobby R. Hunt

Proceedings Volume 2302, Image Reconstruction and Restoration; (1994) https://doi.org/10.1117/12.188036
Event: SPIE's 1994 International Symposium on Optics, Imaging, and Instrumentation, 1994, San Diego, CA, United States

Abstract

In this paper, 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 limited optical system is used for the simulated data. The real data is two dimensional optical image data from the Hubble Space Telescope.

Citation Download Citation

Patrick Wingkee Yuen and Bobby R. Hunt "Constraining information for improvements to MAP image super-resolution", Proc. SPIE 2302, Image Reconstruction and Restoration, (30 September 1994); https://doi.org/10.1117/12.188036

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