Robust Super-Resolution Using a Median Filter for Irregular Samples
IbPRIA '09 Proceedings of the 4th Iberian Conference on Pattern Recognition and Image Analysis
A blind superresolution method that works for arbitrary motions and point spread functions
ISCGAV'09 Proceedings of the 9th WSEAS international conference on Signal processing, computational geometry and artificial vision
Image super-resolution based on multi-space sparse representation
ICIMCS '10 Proceedings of the Second International Conference on Internet Multimedia Computing and Service
Coordinate-descent super-resolution and registration for parametric global motion models
Journal of Visual Communication and Image Representation
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Super-resolution (SR) techniques produce a high-resolution image from a set of low-resolution undersampled images. In this paper, we propose a new method for super-resolution that uses sampling theory concepts to derive a noniterative SR algorithm. We first raise the issue of the validity of the data model usually assumed in SR, pointing out that it imposes a band-limited reconstructed image plus a certain type of noise. We propose a sampling theory framework with a prefiltering step that allows us to work with more general data models and also a specific new method for SR that uses Delaunay triangulation and B-splines to build the super-resolved image. The proposed method is noniterative and well posed. We prove its effectiveness against traditional iterative and noniterative SR methods on synthetic and real data. Additionally, we also prove that we can first solve the interpolation problem and then make the deblurring not only when the motion is translational but also when there are rotations and shifts and the imaging system point spread function (PSF) is rotationally symmetric.