Sensing Matrix Design via Capacity Maximization for Block Compressive Sensing Applications
Sign Up to like & getrecommendations! Published in 2019 at "IEEE Transactions on Computational Imaging"
DOI: 10.1109/tci.2018.2884291
Abstract: It is well-established in the compressive sensing (CS) literature that sensing matrices whose elements are drawn from independent random distributions exhibit enhanced reconstruction capabilities. In many CS applications, such as electromagnetic imaging, practical limitations on… read more here.
Keywords: reconstruction; compressive sensing; capacity maximization; sensing matrices ... See more keywords
Bayes-Optimal Convolutional AMP
Sign Up to like & getrecommendations! Published in 2021 at "IEEE Transactions on Information Theory"
DOI: 10.1109/tit.2021.3077471
Abstract: This paper proposes Bayes-optimal convolutional approximate message-passing (CAMP) for signal recovery in compressed sensing. CAMP uses the same low-complexity matched filter (MF) for interference suppression as approximate message-passing (AMP). To improve the convergence property of… read more here.
Keywords: bayes optimal; sensing matrices; optimal convolutional; amp ... See more keywords
Sensing Matrix Design and Sparse Recovery on the Sphere and the Rotation Group
Sign Up to like & getrecommendations! Published in 2020 at "IEEE Transactions on Signal Processing"
DOI: 10.1109/tsp.2020.2973545
Abstract: In this article, the goal is to design deterministic sampling patterns on the sphere and the rotation group and, thereby, construct sensing matrices for sparse recovery of band-limited functions. It is first shown that random… read more here.
Keywords: sensing matrices; sampling patterns; coherence; rotation group ... See more keywords