Now showing items 1-7 of 7

  • Accelerating super-resolution for 4K upscaling 

    Pérez-Pellitero, Eduardo; Salvador, Jordi; Ruiz Hidalgo, Javier; Rosenhahn, Bodo (Institute of Electrical and Electronics Engineers (IEEE), 2015)
    Conference report
    Restricted access - publisher's policy
    This paper presents a fast Super-Resolution (SR) algorithm based on a selective patch processing. Motivated by the observation that some regions of images are smooth and unfocused and can be properly upscaled with fast ...
  • Antipodally invariant metrics for fast regression-based super-resolution 

    Pérez-Pellitero, Eduardo; Salvador, Jordi; Ruiz Hidalgo, Javier; Rosenhahn, Bodo (2016-03-31)
    Article
    Open Access
    Dictionary-based super-resolution (SR) algorithms usually select dictionary atoms based on the distance or similarity metrics. Although the optimal selection of the nearest neighbors is of central importance for such ...
  • Bayesian region selection for adaptive dictionary-based Super-Resolution 

    Pérez-Pellitero, Eduardo; Salvador, Jordi; Ruiz Hidalgo, Javier; Rosenhahn, Bodo (2013)
    Conference lecture
    Open Access
    The performance of dictionary-based super-resolution (SR) strongly depends on the contents of the training dataset. Nevertheless, many dictionary-based SR methods randomly select patches from of a larger set of training ...
  • Fast super-resolution via dense local training and inverse regressor search 

    Pérez-Pellitero, Eduardo; Salvador, Jordi; Torres-Xirau, Iban; Ruiz Hidalgo, Javier; Rosenhahn, Bodo (2014-11-01)
    Article
    Open Access
    Regression-based Super-Resolution (SR) addresses the upscaling problem by learning a mapping function (i.e. regressor) from the low-resolution to the high-resolution manifold. Under the locally linear assumption, this ...
  • Fast super-resolution via dense local training and inverse regressor search 

    Pérez Pellitero, Eduardo; Salvador, Jordi; Torres Xirau, Iban; Ruiz Hidalgo, Javier; Rosenhahn, Bodo (Springer, 2014)
    Conference report
    Restricted access - publisher's policy
    Regression-based Super-Resolution (SR) addresses the upscaling problem by learning a mapping function (i.e. regressor) from the low-resolution to the high-resolution manifold. Under the locally linear assumption, this ...
  • Half hypersphere confinement for piecewise linear regression 

    Pérez-Pellitero, Eduardo; Salvador, Jordi; Ruiz Hidalgo, Javier; Rosenhahn, Bodo (Institute of Electrical and Electronics Engineers (IEEE), 2016)
    Conference report
    Restricted access - publisher's policy
    Recent research in piecewise linear regression for Super-Resolution has shown the positive impact of training regressors with densely populated clusters whose datapoints are tight in the Euclidean space. In this paper we ...
  • PSyCo: manifold span reduction for super resolution 

    Pérez Pellitero, Eduardo; Salvador, Jordi; Ruiz Hidalgo, Javier; Rosenhahn, Bodo (Institute of Electrical and Electronics Engineers (IEEE), 2016)
    Conference report
    Restricted access - publisher's policy
    The main challenge in Super Resolution (SR) is to discover the mapping between the low- and highresolution manifolds of image patches, a complex ill-posed problem which has recently been addressed through piecewise linear ...