Multi-frame blind deconvolution: Compact and multi-channel versions

Computer Science – Performance

Scientific paper

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Scientific paper

We investigate two variations of the classic multi-frame blind deconvolution (MFBD) algorithm. The first is compact MFBD, a technique for reducing the number of unknown variables in the MFBD problem while still maintaining leverage from all available data frames. The second is a multi-channel MFBD algorithm. We demonstrate the performance of the former using real ground-based imagery of a satellite and show that it produces an image with fewer artifacts than were obtained with conventional MFBD. We demonstrate the performance of the latter using simulated imagery from two telescopes with different sized apertures, and show that data included from the smaller aperture telescope can facilitate an improved restoration over that obtained using solely the data from the large aperture telescope. Both variations of the MFBD technique have application in space situational awareness.

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