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Sparse Reconstruction by Separable Approximation

by Stephen J. Wright , Robert D. Nowak , Mário A. T. Figueiredo , 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), wavelet-based deconvolution and reconstruction, and compressed sensing ..."
Abstract - Cited by 373 (38 self) - Add to MetaCart
of minimizing the sum of a smooth convex function and a nonsmooth, possibly nonconvex, sparsity-inducing function. We propose iterative methods in which each step is an optimization subproblem involving a separable quadratic term (diagonal Hessian) plus the original sparsity-inducing term. Our approach

Curvelet-Wavelet Regularized Split Bregman Iteration for Compressed Sensing

by Gerlind Plonka, Jianwei Ma
"... Compressed sensing is a new concept in signal processing. Assuming that a signal can be represented or approximated by only a few suitably chosen terms in a frame expansion, compressed sensing allows to recover this signal from much fewer samples than the Shannon-Nyquist theory requires. Many images ..."
Abstract - Cited by 119 (6 self) - Add to MetaCart
that they can be understood as special cases of the Douglas-Rachford Split algorithm. Numerical experiments for compressed sensing based Fourier-domain random imaging show good performances of the proposed curvelet-wavelet regularized split Bregman (CWSpB) methods,whereweparticularlyuseacombination of wavelet

ITERATIVELY REGULARIZED GAUSS-NEWTON METHOD FOR ATMOSPHERIC REMOTE SENSING APPLIED TO MIPAS AND SCIAMACHY LIMB SOUNDING OBSERVATIONS

by Adrian Doicu, Franz Schreier, Siegfried Hilgers, Albrecht Von Bargen, Er Slijkhuis, Michael Hess, Bernd Aberle
"... In this paper we present a retrieval algorithm for atmospheric remote sensing. The algorithm combines the Tikhonov regularization and the iteratively Gauss–Newton method and is devoted to the solution of multi–parameter inverse problems with simple bounds on the variables. The basic features of the ..."
Abstract - Cited by 5 (3 self) - Add to MetaCart
In this paper we present a retrieval algorithm for atmospheric remote sensing. The algorithm combines the Tikhonov regularization and the iteratively Gauss–Newton method and is devoted to the solution of multi–parameter inverse problems with simple bounds on the variables. The basic features

Regularizing method for the determination of the backscatter cross section in lidar data SIGNALS

by Yanfei Wang , Jianzhong Zhang , Andreas Roncat , Claudia Künzer , Wolfgang Wagner - Optical Society of America , 2009
"... The retrieval of the backscatter cross section in lidar data is of great interest in remote sensing. For the numerical calculation of the backscatter cross section, a deconvolution has to be performed; its determination is therefore an ill-posed problem. Most of the common techniques, such as the w ..."
Abstract - Cited by 2 (0 self) - Add to MetaCart
The retrieval of the backscatter cross section in lidar data is of great interest in remote sensing. For the numerical calculation of the backscatter cross section, a deconvolution has to be performed; its determination is therefore an ill-posed problem. Most of the common techniques

Building boundary tracing and regularization from airborne LiDAR point clouds, Photogrammetric Engineering and Remote Sensing,

by Aparajithan Sampath , Jie Shan , 2007
"... ..."
Abstract - Cited by 28 (0 self) - Add to MetaCart
Abstract not found

Bregman iterative algorithms for ℓ1-minimization with applications to compressed sensing

by Wotao Yin, Stanley Osher, Donald Goldfarb, Jerome Darbon - SIAM J. IMAGING SCI , 2008
"... We propose simple and extremely efficient methods for solving the basis pursuit problem min{‖u‖1: Au = f,u ∈ R n}, which is used in compressed sensing. Our methods are based on Bregman iterative regularization, and they give a very accurate solution after solving only a very small number of 1 insta ..."
Abstract - Cited by 84 (15 self) - Add to MetaCart
We propose simple and extremely efficient methods for solving the basis pursuit problem min{‖u‖1: Au = f,u ∈ R n}, which is used in compressed sensing. Our methods are based on Bregman iterative regularization, and they give a very accurate solution after solving only a very small number of 1

Article Automatic Vehicle Extraction from Airborne LiDAR Data Using an Object-Based Point Cloud Analysis Method

by Jixian Zhang, Minyan Duan, Qin Yan, Xiangguo Lin , 2014
"... remote sensing ..."
Abstract - Cited by 1 (1 self) - Add to MetaCart
remote sensing

Article Shiftable Leading Point Method for High Accuracy Registration of Airborne and Terrestrial LiDAR Data

by Liang Cheng, Lihua Tong, Yang Wu, Yanming Chen, Manchun Li
"... remote sensing ..."
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remote sensing

Article A Thin Plate Spline-Based Feature-Preserving Method for Reducing Elevation Points Derived from LiDAR

by Chuanfa Chen, Yanyan Li, Changqing Yan, Honglei Dai, Guolin Liu , 2015
"... remote sensing ..."
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remote sensing

Article An Effective Method for Detecting Potential Woodland Vernal Pools Using High-Resolution LiDAR Data and Aerial Imagery

by Qiusheng Wu, Charles Lane, Hongxing Liu , 2014
"... remote sensing ..."
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remote sensing
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