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ICIP
2009
IEEE
13 years 5 months ago
Randomness-in-Structured Ensembles for compressed sensing of images
Leading compressed sensing (CS) methods require m = O (k log(n)) compressive samples to perfectly reconstruct a k-sparse signal x of size n using random projection matrices (e.g., ...
Abdolreza A. Moghadam, Hayder Radha
ICIP
2008
IEEE
14 years 9 months ago
Subspace-based methods for image registration and super-resolution
Super-resolution algorithms combine multiple low resolution images into a single high resolution image. They have received a lot of attention recently in various application domai...
Patrick Vandewalle, Loïc Baboulaz, Pier Luigi...
TIP
2011
195views more  TIP 2011»
13 years 2 months ago
Accelerating X-Ray Data Collection Using Pyramid Beam Ray Casting Geometries
Abstract—Image reconstruction from its projections is a necessity in many applications such as medical (CT), security, inspection, and others. This paper extends the 2-D Fan-beam...
Amir Averbuch, Guy Lifschitz, Yoel Shkolnisky
ICCV
2007
IEEE
14 years 9 months ago
A Variational Method for Scene Flow Estimation from Stereo Sequences
This paper presents a method for scene flow estimation from a calibrated stereo image sequence. The scene flow contains the 3-D displacement field of scene points, so that the 2-D...
Frederik Huguet, Frederic Devernay
ICIP
2008
IEEE
14 years 1 months ago
Compressed sensing for multi-view tracking and 3-D voxel reconstruction
Compressed sensing(CS) suggests that a signal, sparse in some basis, can be recovered from a small number of random projections. In this paper, we apply the CS theory on sparse ba...
Dikpal Reddy, Aswin C. Sankaranarayanan, Volkan Ce...