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The Computer Journal Advance Access originally published online on December 10, 2007
The Computer Journal 2009 52(1):142-152; doi:10.1093/comjnl/bxm098
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© The Author 2007. Published by Oxford University Press on behalf of The British Computer Society. All rights reserved. For Permissions, please email: journals.permissions@oxfordjournals.org

Super-Resolution and Blind Deconvolution For Rational Factors With an Application to Color Images

Filip Sroubek1,*, Jan Flusser1 and Gabriel Cristóbal2

1 Institute of Information Theory and Automation, AS CR, Pod vodárenskou vezí 4, 182 08, Prague 8, Czech Republic
2 Instituto de Óptica, CSIC, Serrano 121, 28006 Madrid, Spain

* Corresponding author: sroubekf{at}utia.cas.cz

Received 19 December 2006; revised 24 October 2007

In many real applications, traditional super-resolution (SR) methods fail to provide high-resolution images due to objectionable blur and inaccurate registration of input low-resolution images. Only integer resolution enhancement factors, such as 2 or 3, are often considered, but non-integer factors between 1 and 2 are also important in real cases. We introduce a method to SR and deconvolution, which assumes no prior information about the shape of degradation blurs, incorporates registration parameters, and is properly defined for any rational (fractional) resolution factor. The method minimizes a regularized energy function with respect to the high-resolution image and blurs, where regularization is carried out in both the image and blur domains. The blur regularization is based on a generalized multi-channel blind deconvolution constraint derived in the paper. An extension to color images is briefly discussed. Experiments on real data illustrate robustness to noise and other advantages of the method.

Key Words: super-resolution • blind deconvolution • alternating minimization • polyphase components


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