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Equivalence of Reconstruction from the Absolute Value of the Frame Coefficients to a Sparse Representation Problem
frames nonlinear processing sparse representation
2015/9/29
The purpose of this note is to prove, for real frames, that signal reconstruction from the absolute value ofthe frame coefficients is equivalent to solution of a sparse signal optimization problem, na...
Optimally Sparse Representation in General (non-Orthogonal) Dictionaries via
Sparse Representation Overcomplete Representation
2015/8/21
Given a ‘dictionary’ D = {dk} of vectors dk, we seek to represent a signal S as a
linear combination S =
k γ(k)dk, with scalar coefficients γ(k). In particular, we aim for
the sparsest repre...
Laplace Inversion of Low-Resolution NMR Relaxometry Data Using Sparse Representation Methods
low-resolution NMR sparse reconstruction
2015/7/3
Low-resolution nuclear magnetic resonance (LR-NMR) relaxometry is a
powerful tool that can be harnessed for characterizing constituents in complex materials.
Enhancement of snow cover change detection with sparse representation and dictionary learning
Snow Cover Change Detection NDSI Sparse Representation K-SVD k-means clustering
2014/12/15
Sparse representation and decoding is often used for denoising images and compression of images with respect to inherent features. In this paper, we adopt a methodology incorporating sparse representa...
Learning Stable Multilevel Dictionaries for Sparse Representation of Images
Learning Stable Multilevel Dictionaries Sparse Representation Images
2013/4/28
Dictionaries adapted to the data provide superior performance when compared to predefined dictionaries in applications involving sparse representations. Algorithmic stability and generalization are de...
SPARSE REPRESENTATION BASED VISUAL ELEMENT ANALYSIS
Clothes recommendation independent component analysis term frequency style mining
2013/7/24
Modern clothes are designed based on various visualelements of different fashion styles. Traditional vision-based clothes recommendation methods focused on searchingclothes which are similar with user...
Optimal Sparse Representation for Blind Deconvolution of Images
Optimal Sparse Representation Blind Deconvolution Images
2010/1/7
The relative Newton algorithm, previously proposed for quasi maximum likelihood blind source separation and blind deconvolution of one-dimensional signals is generalized for blind deconvolution of ima...
Optimal Sparse Representation for Blind Deconvolution of Images
Optimal Sparse Representation Blind Deconvolution Images
2010/1/7
The relative Newton algorithm, previously proposed for quasi maximum likelihood blind source separation and blind deconvolution of one-dimensional signals is generalized for blind deconvolution of ima...
Classification with minimax fast rates for classes of Bayes rules with sparse representation
Classification Sparsity Decision dyadic trees Minimax rates Aggregation
2009/9/16
We consider the classification problem on the cube $[0,1]^d$ when the Bayes rule is known to belong to some new functions classes. These classes are made of prediction rules satisfying some conditions...