The goal of this thesis is to demonstrate practical application of sparse data representation in the processing of sparse signals. For solving several example problems - denoising, dequantization, and sparse signal decomposition - convex optimization was used. The solutions were implemented in the Matlab environment. For each of the problems, there are two solutions - one for one-dimensional, and one for two-dimensional signal
We propose recovering 1D piecewice linear signal using a sparsity-based method consisting of two ste...
Analysis sparse representation has recently emerged as an alternative approach to the synthesis spar...
Estimation of a sparse signal representation, one with the minimum number of nonzero components, is ...
An overview is given of the role of the sparseness constraint in signal processing problems. It is s...
In pattern recognition and machine learning, a classification problem refers to finding an algorithm...
In pattern recognition and machine learning, a classification problem refers to finding an algorithm...
In this paper, application of sparse representation (factorization) of signals over an overcomplete ...
Many problems in signal processing and statistical inference are based on finding a sparse solution ...
The topic of this thesis is sparse representations of signals. The thesis follows mainly the book Sp...
Research Doctorate - Doctor of Philosophy (PhD)A vector is called sparse when most of its components...
Sparse representation has attracted much attention from researchers in fields of signal processing, ...
Sparse representation is an active research topic in signal and image processing because of its vast...
Sparse representation has attracted much attention from researchers in fields of signal processing, ...
Most of the naturally occurring signals typically carry overwhelming amounts of data in which releva...
Abstract—This paper addresses the problem of sparsity penal-ized least squares for applications in s...
We propose recovering 1D piecewice linear signal using a sparsity-based method consisting of two ste...
Analysis sparse representation has recently emerged as an alternative approach to the synthesis spar...
Estimation of a sparse signal representation, one with the minimum number of nonzero components, is ...
An overview is given of the role of the sparseness constraint in signal processing problems. It is s...
In pattern recognition and machine learning, a classification problem refers to finding an algorithm...
In pattern recognition and machine learning, a classification problem refers to finding an algorithm...
In this paper, application of sparse representation (factorization) of signals over an overcomplete ...
Many problems in signal processing and statistical inference are based on finding a sparse solution ...
The topic of this thesis is sparse representations of signals. The thesis follows mainly the book Sp...
Research Doctorate - Doctor of Philosophy (PhD)A vector is called sparse when most of its components...
Sparse representation has attracted much attention from researchers in fields of signal processing, ...
Sparse representation is an active research topic in signal and image processing because of its vast...
Sparse representation has attracted much attention from researchers in fields of signal processing, ...
Most of the naturally occurring signals typically carry overwhelming amounts of data in which releva...
Abstract—This paper addresses the problem of sparsity penal-ized least squares for applications in s...
We propose recovering 1D piecewice linear signal using a sparsity-based method consisting of two ste...
Analysis sparse representation has recently emerged as an alternative approach to the synthesis spar...
Estimation of a sparse signal representation, one with the minimum number of nonzero components, is ...