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ICASSP
2011
IEEE
13 years 23 days ago
Using the kernel trick in compressive sensing: Accurate signal recovery from fewer measurements
Compressive sensing accurately reconstructs a signal that is sparse in some basis from measurements, generally consisting of the signal’s inner products with Gaussian random vec...
Hanchao Qi, Shannon Hughes
CORR
2012
Springer
218views Education» more  CORR 2012»
12 years 4 months ago
Robust 1-bit compressed sensing and sparse logistic regression: A convex programming approach
This paper develops theoretical results regarding noisy 1-bit compressed sensing and sparse binomial regression. We demonstrate that a single convex program gives an accurate estim...
Yaniv Plan, Roman Vershynin
CVPR
2009
IEEE
15 years 4 months ago
Stereo Matching in the Presence of Sub-Pixel Calibration Errors
Stereo matching commonly requires rectified images that are computed from calibrated cameras. Since all under- lying parametric camera models are only approximations, calibratio...
Heiko Hirschmüller, Stefan K. Gehrig
CVPR
2009
IEEE
2160views Computer Vision» more  CVPR 2009»
15 years 4 months ago
Image Deblurring and Denoising using Color Priors
Image blur and noise are difficult to avoid in many situations and can often ruin a photograph. We present a novel image deconvolution algorithm that deblurs and denoises an ima...
C. Lawrence Zitnick, David J. Kriegman, Neel Joshi...
CVPR
2000
IEEE
14 years 11 months ago
Reconstruction from Six-Point Sequences
An algorithm is given for computing projective structure from a set of six points seen in a sequence of many images. The method is based on the notion of duality between cameras a...
Richard I. Hartley, Nicolas Y. Dano