Online multi-frame blind deconvolution with super-resolution and saturation correction

Details Online multi-frame blind deconvolution with super-resolution and saturation correction Online multi-frame blind deconvolution with super-resolution and saturation correction

Jul 2011

adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=2011a%26a...531a...9h&link_type=abstract

Astronomy & Astrophysics, Volume 531, id.A9

Astronomy and Astrophysics

Astrophysics

3

Methods: Numerical, Techniques: Image Processing, Techniques: Miscellaneous, Atmospheric Effects

Scientific paper

Astronomical images taken by ground-based telescopes suffer degradation due to atmospheric turbulence. This degradation can be tackled by costly hardware-based approaches such as adaptive optics, or by sophisticated software-based methods such as lucky imaging, speckle imaging, or multi-frame deconvolution. Software-based methods process a sequence of images to reconstruct a deblurred high-quality image. However, existing approaches are limited in one or several aspects: (i) they process all images in batch mode, which for thousands of images is prohibitive; (ii) they do not reconstruct a super-resolved image, even though an image sequence often contains enough information; (iii) they are unable to deal with saturated pixels; and (iv) they are usually non-blind, i.e., they assume the blur kernels to be known. In this paper we present a new method for multi-frame deconvolution called online blind deconvolution (OBD) that overcomes all these limitations simultaneously. Encouraging results on simulated and real astronomical images demonstrate that OBD yields deblurred images of comparable and often better quality than existing approaches.

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