Sparse Recovery for Overcomplete Frames: Sensing Matrices and Recovery Guarantees
Signal models formed as linear combinations of few atoms from an over-complete dictionary
or few frame vectors from a redundant frame have become central to many applications in …
or few frame vectors from a redundant frame have become central to many applications in …
A unified recovery of structured signals using atomic norm
X Chen - Information and Inference: A Journal of the IMA, 2024 - academic.oup.com
In many applications, we seek to recover signals from linear measurements far fewer than
the ambient dimension, given the signals have exploitable structures such as sparse vectors …
the ambient dimension, given the signals have exploitable structures such as sparse vectors …
Signal separation under coherent dictionaries and ℓp-bounded noise
Y Xia, S Li - Journal of Approximation Theory, 2021 - Elsevier
In this paper, we discuss the compressed data separation problem. In order to reconstruct
the distinct subcomponents, which are sparse in morphologically different dictionaries D 1∈ …
the distinct subcomponents, which are sparse in morphologically different dictionaries D 1∈ …
Dictionary-sparse recovery from heavy-tailed measurements
P Abdalla, C Kümmerle - Information and Inference: A Journal of …, 2022 - academic.oup.com
The recovery of signals that are sparse not in a given basis, but rather sparse with respect to
an over-complete dictionary is one of the most flexible settings in the field of compressed …
an over-complete dictionary is one of the most flexible settings in the field of compressed …