Image restoration and superresolution as probes of small scale far-IR structure in star forming regions

Details Image restoration and superresolution as probes of small scale far-IR structure in star forming regions Image restoration and superresolution as probes of small scale far-IR structure in star forming regions

Oct 1986

adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=1986inpr.conf....9l&link_type=abstract

In NASA. Ames Research Center Summer School on Interstellar Processes: Abstracts of Contributed Papers p 9-10 (SEE N87-15043 06

Astronomy and Astrophysics

Astronomy

Image Processing, Infrared Astronomy, Interstellar Matter, Spatial Filtering, Spatial Resolution, Star Formation, Algorithms, Maximum Entropy Method, Planetary Nebulae, Spiral Galaxies

Scientific paper

Far-infrared continuum studies from the Kuiper Airborne Observatory are described that are designed to fully exploit the small-scale spatial information that this facility can provide. This work gives the clearest picture to data on the structure of galactic and extragalactic star forming regions in the far infrared. Work is presently being done with slit scans taken simultaneously at 50 and 100 microns, yielding one-dimensional data. Scans of sources in different directions have been used to get certain information on two dimensional structure. Planned work with linear arrays will allow us to generalize our techniques to two dimensional image restoration. For faint sources, spatial information at the diffraction limit of the telescope is obtained, while for brighter sources, nonlinear deconvolution techniques have allowed us to improve over the diffraction limit by as much as a factor of four. Information on the details of the color temperature distribution is derived as well. This is made possible by the accuracy with which the instrumental point-source profile (PSP) is determined at both wavelengths. While these two PSPs are different, data at different wavelengths can be compared by proper spatial filtering. Considerable effort has been devoted to implementing deconvolution algorithms. Nonlinear deconvolution methods offer the potential of superresolution -- that is, inference of power at spatial frequencies that exceed D lambda. This potential is made possible by the implicit assumption by the algorithm of positivity of the deconvolved data, a universally justifiable constraint for photon processes. We have tested two nonlinear deconvolution algorithms on our data; the Richardson-Lucy (R-L) method and the Maximum Entropy Method (MEM). The limits of image deconvolution techniques for achieving spatial resolution are addressed.

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