Andere Kunden interessierten sich auch für
![Compressed Sensing & Sparse Filtering]( "Compressed Sensing & Sparse Filtering")
Compressed Sensing & Sparse Filtering115,99 €![Compressed Sensing und Sparse Rekonstruktion bei Magnetic Particle Imaging]( "Compressed Sensing und Sparse Rekonstruktion bei Magnetic Particle Imaging")
Compressed Sensing und Sparse Rekonstruktion bei Magnetic Particle Imaging49,90 €![Sparse Signal Representation for a single-image Super Resolution]( "Sparse Signal Representation for a single-image Super Resolution")
Sparse Signal Representation for a single-image Super Resolution26,99 €![Compressed Sensing for Privacy-Preserving Data Processing]( "Compressed Sensing for Privacy-Preserving Data Processing")
Compressed Sensing for Privacy-Preserving Data Processing37,99 €![Bayesian Deconvolution of Sparse Processes]( "Bayesian Deconvolution of Sparse Processes")
Bayesian Deconvolution of Sparse Processes32,99 €![A Novel Shoeprint Enhancement method Using Sparse Representation]( "A Novel Shoeprint Enhancement method Using Sparse Representation")
A Novel Shoeprint Enhancement method Using Sparse Representation23,99 €![Sparse Adaptive Filtering Techniques for Channel Estimation]( "Sparse Adaptive Filtering Techniques for Channel Estimation")
Sparse Adaptive Filtering Techniques for Channel Estimation23,99 €
The presented work revolves around sparsity. It contributes to dictionary training for sparse representation with a new algorithm and analysis. It showcases the usability of trained dictionary in image processing problems. It demonstrates a new framework for image recovery (inpainting and denoising) using sparse representation. In the end, it proposes an extension of the well-known sparse signal recovery algorithm, Orthogonal Matching Pursuit (OMP) for compressed sensing. It also provides a complete analysis of the proposed extension, and its theoretical guarantees.