Theoretical Analysis on Reconstruction-Based Super-Resolution for an Arbitrary PSF

Authors:
Masayuki Tanaka;Masatoshi Okutomi
Affiliations:
Tokyo Institute of Technology;Tokyo Institute of Technology
Venue:
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 2 - Volume 02
Year:
2005

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Abstract

This study presents and proves a condition number theorem for super-resolution (SR). The SR condition number theorem provides the condition number for an arbitrary space-invariant point spread function (PSF) when using aninfinite number of low resolution images. A gradient restriction is also derived for maximum likelihood (ML) method. The gradient restriction is presented as an inequality which shows that the power spectrum of the PSF suppresses the spatial frequency component of the gradient of ML cost function. A Box PSF and a Gaussian PSF are analyzed with the SR condition number theorem. Effects of the gradient restriction on super-resolution results are shown using synthetic images.