3 papers
cs.IT2020
Robust Instance-Optimal Recovery of Sparse Signals at Unknown Noise Levels
Hendrik Bernd Petersen, Peter Jung
We consider the problem of sparse signal recovery from noisy measurements. Many of frequently used recovery methods rely on some sort of tuning depending on either noise or signal…
cs.IT2020
Compressed sensing-based SARS-CoV-2 pool testing
Hendrik Bernd Petersen, Bubacarr Bah, Peter Jung
We propose a compressed sensing-based testing approach with a practical measurement design and a tuning-free and noise-robust algorithm for detecting infected persons. Compressed s…
cs.IT2020
Efficient Tuning-Free -Regression of Nonnegative Compressible Signals
Hendrik Bernd Petersen, Bubacarr Bah, Peter Jung
In compressed sensing the goal is to recover a signal from as few as possible noisy, linear measurements. The general assumption is that the signal has only a few non-zero entries.…