Compressed Sensing With Prior Information: Strategies, Geometry, and Bounds

DOI10.1109/TIT.2017.2695614zbMath1370.94197MaRDI QIDQ5358584

Nikos Deligiannis, Miguel R. D. Rodrigues, João F. C. Mota

Publication date: 21 September 2017

Published in: IEEE Transactions on Information Theory (Search for Journal in Brave)

Mathematics Subject Classification ID

Signal theory (characterization, reconstruction, filtering, etc.) (94A12)

Related Items (6)

Exploiting prior knowledge in compressed sensing to design robust systems for endoscopy image recovery ⋮ Recovery error analysis of noisy measurement in compressed sensing ⋮ Stable recovery of weighted sparse signals from phaseless measurements via weighted l₁ minimization ⋮ Combined similarity to reference image with joint sparsifying transform for longitudinal compressive sensing MRI ⋮ New conditions on stable recovery of weighted sparse signals via weighted \(l_1\) minimization ⋮ A simple recovery framework for signals with time-varying sparse support

This page was built for publication: Compressed Sensing With Prior Information: Strategies, Geometry, and Bounds