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A volumetric method for building complex models from range images,”
 in Proceedings of the 23rd annual conference on Computer graphics and interactive techniques. ACM,
, 1996
"... Abstract A number of techniques have been developed for reconstructing surfaces by integrating groups of aligned range images. A desirable set of properties for such algorithms includes: incremental updating, representation of directional uncertainty, the ability to fill gaps in the reconstruction, ..."
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Cited by 1020 (17 self)
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Abstract A number of techniques have been developed for reconstructing surfaces by integrating groups of aligned range images. A desirable set of properties for such algorithms includes: incremental updating, representation of directional uncertainty, the ability to fill gaps in the reconstruction
Sparse signal reconstruction from limited data using FOCUSS: A reweighted minimum norm algorithm
 IEEE TRANS. SIGNAL PROCESSING
, 1997
"... We present a nonparametric algorithm for finding localized energy solutions from limited data. The problem we address is underdetermined, and no prior knowledge of the shape of the region on which the solution is nonzero is assumed. Termed the FOcal Underdetermined System Solver (FOCUSS), the algor ..."
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Cited by 368 (22 self)
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), the algorithm has two integral parts: a lowresolution initial estimate of the real signal and the iteration process that refines the initial estimate to the final localized energy solution. The iterations are based on weighted norm minimization of the dependent variable with the weights being a function
Sparse Reconstruction by Separable Approximation
, 2007
"... Finding sparse approximate solutions to large underdetermined linear systems of equations is a common problem in signal/image processing and statistics. Basis pursuit, the least absolute shrinkage and selection operator (LASSO), waveletbased deconvolution and reconstruction, and compressed sensing ..."
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Cited by 373 (38 self)
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Finding sparse approximate solutions to large underdetermined linear systems of equations is a common problem in signal/image processing and statistics. Basis pursuit, the least absolute shrinkage and selection operator (LASSO), waveletbased deconvolution and reconstruction, and compressed sensing
An EM Algorithm for WaveletBased Image Restoration
, 2002
"... This paper introduces an expectationmaximization (EM) algorithm for image restoration (deconvolution) based on a penalized likelihood formulated in the wavelet domain. Regularization is achieved by promoting a reconstruction with lowcomplexity, expressed in terms of the wavelet coecients, taking a ..."
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Cited by 352 (22 self)
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This paper introduces an expectationmaximization (EM) algorithm for image restoration (deconvolution) based on a penalized likelihood formulated in the wavelet domain. Regularization is achieved by promoting a reconstruction with lowcomplexity, expressed in terms of the wavelet coecients, taking
Accelerated Image Reconstruction using Ordered Subsets of Projection Data
 IEEE TRANS. MED. IMAG
, 1994
"... We define ordered subset processing for standard algorithms (such as Expectation Maximization, EM) for image restoration from projections. Ordered subsets methods group projection data into an ordered sequence of subsets (or blocks). An iteration of ordered subsets EM is defined as a single pass thr ..."
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Cited by 301 (2 self)
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We define ordered subset processing for standard algorithms (such as Expectation Maximization, EM) for image restoration from projections. Ordered subsets methods group projection data into an ordered sequence of subsets (or blocks). An iteration of ordered subsets EM is defined as a single pass
A new alternating minimization algorithm for total variation image reconstruction
 SIAM J. IMAGING SCI
, 2008
"... We propose, analyze and test an alternating minimization algorithm for recovering images from blurry and noisy observations with total variation (TV) regularization. This algorithm arises from a new halfquadratic model applicable to not only the anisotropic but also isotropic forms of total variati ..."
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Cited by 224 (26 self)
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variation discretizations. The periteration computational complexity of the algorithm is three Fast Fourier Transforms (FFTs). We establish strong convergence properties for the algorithm including finite convergence for some variables and relatively fast exponential (or qlinear in optimization
Nonuniform Sampling and Reconstruction in ShiftInvariant Spaces
, 2001
"... This article discusses modern techniques for nonuniform sampling and reconstruction of functions in shiftinvariant spaces. It is a survey as well as a research paper and provides a unified framework for uniform and nonuniform sampling and reconstruction in shiftinvariant spaces by bringing togeth ..."
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Cited by 218 (13 self)
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of shiftinvariant spaces. (b) The general theory works in arbitrary dimension and for a broad class of generators. (c) The reconstruction of a function from any sufficiently dense nonuniform sampling set is obtained by efficient iterative algorithms. These algorithms converge geometrically and are robust
RECONSTRUCTION IN COMPRESSIVE SENSING USING AFFINE SCALING TRANSFORMATIONS WITH VARIABLEp DIVERSITY MEASURE
"... The Affine Scaling Transformation (AST) family of algorithms can solve the minimization of the �(p�1), pnormlike diversity measure for an underdetermined linear inverse problem. The AST algorithms can therefore be used to solve the sparse signal recovery problem that arises in Compressive Sensing. ..."
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the values of p as a function of the iteration in the AST algorithm. The goal in these strategies for a variablep AST algorithm is to capture the benefits of the p=0 and the p=1 fixedp approaches simultaneously. Index Terms — Affine Scaling Transformation, diversity measure, compressive sensing, sparse
Penalized Weighted LeastSquares Image Reconstruction for Positron Emission Tomography
 IEEE TR. MED. IM
, 1994
"... This paper presents an image reconstruction method for positronemission tomography (PET) based on a penalized, weighted leastsquares (PWLS) objective. For PET measurements that are precorrected for accidental coincidences, we argue statistically that a leastsquares objective function is as approp ..."
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Cited by 118 (44 self)
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This paper presents an image reconstruction method for positronemission tomography (PET) based on a penalized, weighted leastsquares (PWLS) objective. For PET measurements that are precorrected for accidental coincidences, we argue statistically that a leastsquares objective function
Recovering 3D Shape and Motion from Image Streams using NonLinear Least Squares
, 1993
"... The simultaneous recovery of 3D shape and motion from image sequences is one of the more difficult problems in computer vision. Classical approaches to the problem rely on using algebraic techniques to solve for these unknowns given two or more images. More recently, a batch analysis of image stream ..."
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Cited by 215 (33 self)
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streams (the temporal tracks of distinguishable image features) under orthography has resulted in highly accurate reconstructions. We generalize this approach to perspective projection and partial or uncertain tracks by using a nonlinear least squares technique. While our approach requires iteration
Results 1  10
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