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A tutorial on sparse signal reconstruction and its applications in signal processing
Sparse signals are characterized by a few nonzero coefficients in one of their transformation
domains. This was the main premise in designing signal compression algorithms …
domains. This was the main premise in designing signal compression algorithms …
Analysis of the reconstruction of sparse signals in the DCT domain applied to audio signals
Sparse signals can be reconstructed from a reduced set of signal samples using
compressive sensing (CS) methods. The discrete cosine transform (DCT) can provide highly …
compressive sensing (CS) methods. The discrete cosine transform (DCT) can provide highly …
On the reconstruction of nonsparse time-frequency signals with sparsity constraint from a reduced set of samples
Nonstationary signals, approximately sparse in the joint time-frequency domain, are
considered. Reconstruction of such signals with sparsity constraint is analyzed in this paper …
considered. Reconstruction of such signals with sparsity constraint is analyzed in this paper …
On the errors in randomly sampled nonsparse signals reconstructed with a sparsity assumption
An analysis of errors in the reconstruction of approximately sparse and nonsparse noisy
signals in the discrete Fourier transform domain is considered in this letter. Signal …
signals in the discrete Fourier transform domain is considered in this letter. Signal …
Compressive sensing of sparse signals in the Hermite transform basis
An analysis of the influence of missing samples in signals exhibiting sparsity in the Hermite
transform domain is presented. Based on the statistical properties derived for the Hermite …
transform domain is presented. Based on the statistical properties derived for the Hermite …
Error in the reconstruction of nonsparse images
Sparse signals, assuming a small number of nonzero coefficients in a transformation
domain, can be reconstructed from a reduced set of measurements. In practical applications …
domain, can be reconstructed from a reduced set of measurements. In practical applications …
Bit-depth quantization and reconstruction error in digital images
Digital images can be considered as sparse or approximately sparse in the two-dimensional
discrete cosine transform domain. According to the compressive sensing theory, these …
discrete cosine transform domain. According to the compressive sensing theory, these …
Analysis of off-grid effects in wideband sonar images using compressive sensing
In this paper an analysis of sparse wideband sonar images, obtained using compressive
sensing reconstruction methods, for generally positioned off-grid targets, is presented. An …
sensing reconstruction methods, for generally positioned off-grid targets, is presented. An …
Analysis of noise and nonsparsity in the ISAR image recovery from a reduced set of data
Sparse inverse synthetic aperture radar (ISAR) images can be reconstructed using a
reduced set of data and compressive sensing based theory. In real cases the ISAR images …
reduced set of data and compressive sensing based theory. In real cases the ISAR images …
Time-frequency signal reconstruction of nonsparse audio signals
In this paper, the reconstruction of non-stationary audio signals is considered. Audio signals
are approximately sparse in the joint time-frequency representation domain. The …
are approximately sparse in the joint time-frequency representation domain. The …