PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Online Multi-frame Blind Deconvolution with Super-resolution and Saturation Correction
Michael Hirsch, Stefan Harmeling, Suvrit Sra and Bernhard Schölkopf
Astronomy and Astrophysics 2009.


Astronomical images taken by ground-based telescopes suffer degradation due to atmospheric turbulence. This degradation can be tackled by expensive hardware-based approaches, such as adaptive optics, or by sophisticated software-based methods such as lucky imaging, speckle imaging, or multi-frame deconvolution. The software-based methods process a sequence of image frames to recon- struct 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 that the blur kernels are 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.

EPrint Type:Article
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Theory & Algorithms
ID Code:7905
Deposited By:Stefan Harmeling
Deposited On:17 March 2011