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 an infinite 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.
Theoretical analysis on reconstruction-based super-resolution for an arbitrary PSF
2005-01-01
282759 byte
Conference paper
Electronic Resource
English
Wavelet-Based Super-Resolution Reconstruction: Theory and Algorithm
British Library Conference Proceedings | 2006
|Fast MAP-based multiframe super-resolution image reconstruction
British Library Online Contents | 2005
|Single Image Super-resolution Reconstruction Method
British Library Online Contents | 2004
|