19,465 research outputs found
An Improved Observation Model for Super-Resolution under Affine Motion
Super-resolution (SR) techniques make use of subpixel shifts between frames
in an image sequence to yield higher-resolution images. We propose an original
observation model devoted to the case of non isometric inter-frame motion as
required, for instance, in the context of airborne imaging sensors. First, we
describe how the main observation models used in the SR literature deal with
motion, and we explain why they are not suited for non isometric motion. Then,
we propose an extension of the observation model by Elad and Feuer adapted to
affine motion. This model is based on a decomposition of affine transforms into
successive shear transforms, each one efficiently implemented by row-by-row or
column-by-column 1-D affine transforms.
We demonstrate on synthetic and real sequences that our observation model
incorporated in a SR reconstruction technique leads to better results in the
case of variable scale motions and it provides equivalent results in the case
of isometric motions
EpicFlow: Edge-Preserving Interpolation of Correspondences for Optical Flow
We propose a novel approach for optical flow estimation , targeted at large
displacements with significant oc-clusions. It consists of two steps: i) dense
matching by edge-preserving interpolation from a sparse set of matches; ii)
variational energy minimization initialized with the dense matches. The
sparse-to-dense interpolation relies on an appropriate choice of the distance,
namely an edge-aware geodesic distance. This distance is tailored to handle
occlusions and motion boundaries -- two common and difficult issues for optical
flow computation. We also propose an approximation scheme for the geodesic
distance to allow fast computation without loss of performance. Subsequent to
the dense interpolation step, standard one-level variational energy
minimization is carried out on the dense matches to obtain the final flow
estimation. The proposed approach, called Edge-Preserving Interpolation of
Correspondences (EpicFlow) is fast and robust to large displacements. It
significantly outperforms the state of the art on MPI-Sintel and performs on
par on Kitti and Middlebury
Spectral filtering for the reduction of the Gibbs phenomenon of polynomial approximation methods on Lissajous curves with applications in MPI
Polynomial interpolation and approximation methods on sampling points along Lissajous curves using Chebyshev series is an effective way for a fast image reconstruction in Magnetic Particle Imaging. Due to the nature of spectral methods, a Gibbs phenomenon occurs in the reconstructed image if the underlying function has discontinuities. A possible solution for this problem are spectral filtering methods acting on the coefficients of the approximating polynomial. In this work, after a description of the Gibbs phenomenon and classical filtering techniques in one and several dimensions, we present an adaptive spectral filtering process for the resolution of this phenomenon and for an improved approximation of the underlying function or image. In this adaptive filtering technique, the spectral filter depends on the distance of a spatial point to the nearest discontinuity. We show the effectiveness of this filtering approach in theory, in numerical simulations as well as in the application in Magnetic Particle Imaging
Robust eye tracking based on multiple corneal reflections for clinical applications
Postprint (published version
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