Super-Resolution Power and Robustness of Compressive Sensing for Spectral Estimation With Application to Spaceborne Tomographic SAR

By Xiaoxiang Zhu, Richard Bamler. IEEE Transactions on Geoscience and Remote Sensing, 2012

336 citations recorded by the source corpus.

Subfields: Aerospace Engineering, Biomedical Engineering, Computational Mechanics

View Paper: https://www.semanticscholar.org/paper/940b9a04b5ccd0d36d254adbf4f902d4373fbcbf

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Aerospace Engineering

1. Paolo Rocca
2. Andrea Massa
3. Giacomo Oliveri
4. Richard Bamler
5. Xiaoxiang Zhu
6. Robert J. Mailloux
7. Benjamin Fuchs
8. Luca Manica

In Biomedical Engineering

1. Paolo Rocca
2. Giacomo Oliveri
3. Andrea Massa
4. Xiaoxiang Zhu
5. Richard Bamler

In Computational Mechanics

1. Paolo Rocca
2. Giacomo Oliveri
3. Andrea Massa
4. Richard Bamler
5. Xiaoxiang Zhu

Citations in the corpus

• A Sparse Image Fusion Algorithm With Application to Pan-Sharpening (2013, methodology): “Hence, we estimate the sparse coefficient α̂k using the SL1MMER algorithm [13], [14], which includes a model order selection step [15] and a debiasing [12] step to get rid of the mentioned amplitude bias.”
• Compressive Sensing in Electromagnetics - A Review (2015, methodology): “, the “ SL1MMER” technique) have been proposed by combining ‘1minimization CS steps with a model order reduction and a maximum-likelihood parameter selection [94].”
• A Systematic Review of Compressive Sensing: Concepts, Implementations and Applications (2018, neutral): “[127]–[129].”