Orthogonal Matching Pursuit with Tikhonov and Landweber Regularization
Robert Seidel

TL;DR
This paper introduces two regularized variants of Orthogonal Matching Pursuit that do not require prior sparsity knowledge, enabling efficient hardware implementation and effective compressed sensing with limited measurements.
Contribution
The paper proposes novel regularized OMP algorithms with no need for prior sparsity, suitable for real-world applications and hardware implementation.
Findings
Good performance with small number of measurements
Regularization improves robustness of OMP
Algorithms are hardware-efficient
Abstract
The Orthogonal Matching Pursuit (OMP) for compressed sensing iterates over a scheme of support augmentation and signal estimation. We present two novel matching pursuit algorithms with intrinsic regularization of the signal estimation step that do not rely on a priori knowledge of the signal's sparsity. An iterative approach allows for a hardware efficient implementation of our algorithm, and enables real-world applications of compressed sensing. We provide a series of numerical examples that demonstrate a good performance, especially when the number of measurements is relatively small.
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsSparse and Compressive Sensing Techniques · Microwave Imaging and Scattering Analysis · Random lasers and scattering media
