PSyCo: Manifold Span Reduction for Super Resolution
Eduardo Pérez-Pellitero (Leibniz Universität Hannover, Technicolor)
Jordi Salvador (Technicolor)
Javier Ruiz-Hidalgo (Universitat Politècnica de Catalunya)
Bodo Rosenhahn (Leibniz Universität Hannover)
IEEE Conference on Computer Vision and Pattern Recognition, 2016
You will soon find more resources about this project in Eduardo's blog.
Abstract
The main challenge in Super Resolution (SR) is to discover the mapping between the low- and high-resolution
manifolds of image patches, a complex ill-posed problem which has recently been addressed through piecewise linear regression with promising results. In this paper we present a novel regression-based SR algorithm that benefits from an extended knowledge of the structure of both manifolds. We propose a transform that collapses the 16 variations induced from the dihedral group of transforms (i.e. rotations,
vertical and horizontal reflections) and antipodality (i.e. diametrically opposed points on the unitary sphere) into a single primitive. The key idea of our transform is to study the different dihedral elements as a group of symmetries within the high-dimensional manifold. We obtain the respective set of mirror-symmetry axes by means of a frequency analysis of the dihedral elements, and we use them to collapse
the redundant variability through a modified symmetry distance. The experimental validation of our algorithm shows the effectiveness of our approach, which obtains competitive quality with a dictionary of as little as 32 atoms (reducing other methods' dictionaries by at least a factor of 32) and further pushing the state of the art with a 1024 atoms dictionary.
BibTeX
@inproceedings { PerezPellitero2016c,author = {P\'erez-Pellitero, E. and Salvador, J. and Ruiz-Hidalgo, J. and Rosenhahn, B.},
title = {{PSyCo: Manifold Span Reduction for Super Resolution}},
booktitle = {Proc. {IEEE} Conf. on Computer Vision and Pattern Recognition},
pages = {first--last},
year = {2016},
}
