<abstract xmlns="http://www.w3.org/1999/xhtml">A nonconventional image algorithm, based on compressed sensing and l1-norm minimization in Synthetic Aperture Radar (SAR) application is discussed. A discrete model of the earth surface relief and mathematical modeling of SAR signal formation are analytically described. Sparse decomposition in Fourier basis to solve the SAR image reconstruction problem is discussed. In contrast to the classical one-dimensional definition of l1-norm minimization in SAR image reconstruction, applied to an image vector, the present work proposes a two-dimensional definition of l1-norm minimization to the image. To verify the correctness of the algorithm, results of numerical experiments are presented.</abstract>

A nonconventional image algorithm, based on compressed sensing and l1-norm minimization in Synthetic Aperture Radar (SAR) application is discussed. A discrete model of the earth surface relief and mathematical modeling of SAR signal formation are analytically described. Sparse decomposition in Fourier basis to solve the SAR image reconstruction problem is discussed. In contrast to the classical one-dimensional definition of l1-norm minimization in SAR image reconstruction, applied to an image vector, the present work proposes a two-dimensional definition of l1-norm minimization to the image. To verify the correctness of the algorithm, results of numerical experiments are presented.

Two-Dimensional <italic>l</italic>1-Norm Minimization in SAR Image Reconstriction

Faculty of Computer Science and Engineering, Burgas Free University, Burgas, Bulgaria

Cybernetics and Information Technologies

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Two-Dimensional -Norm Minimization in SAR Image Reconstriction

A nonconventional image algorithm, based on compressed sensing and l1-norm minimization in Synthetic Aperture Radar (SAR) application is discussed. A discrete...

Two-Dimensional l₁-Norm Minimization in SAR Image Reconstriction

Online veröffentlicht: 19. Jan. 2016

Seitenbereich: 77 - 87

DOI: https://doi.org/10.1515/cait-2015-0091

Schlüsselwörter
SAR image reconstruction, compressed sensing, 2D l-norm minimization

© 2015 A. Lazarov et al., published by De Gruyter Open

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Two-Dimensional l1-Norm Minimization in SAR Image Reconstriction

Online veröffentlicht: 19. Jan. 2016

Seitenbereich: 77 - 87

DOI: https://doi.org/10.1515/cait-2015-0091

SchlüsselwörterSAR image reconstruction, compressed sensing, 2D l-norm minimization

© 2015 A. Lazarov et al., published by De Gruyter Open

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Two-Dimensional l₁-Norm Minimization in SAR Image Reconstriction

Schlüsselwörter
SAR image reconstruction, compressed sensing, 2D l-norm minimization