Efficient Sparse Recovery With Arctangent Regularization: A Novel Iterative Thresholding Algorithm

Several existing works have revealed the effectiveness of arctangent-type penalties in exploiting sparsity for compressed sensing. However, addressing the subproblems associated with the arctangent penalty incurs considerable computational cost. Aiming to reduce complexity, we derive the closed-form...

Ausführliche Beschreibung

Gespeichert in:
Bibliographische Detailangaben
Veröffentlicht in:IEEE transactions on circuits and systems for video technology Jg. 35; H. 6; S. 5367 - 5379
Hauptverfasser: He, Zihao, Shu, Qianyu, Wen, Jinming, Cheung So, Hing
Format: Journal Article
Sprache:Englisch
Veröffentlicht: New York IEEE 01.06.2025
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:
Algorithms
Approximation algorithms
arctangent penalty
Compressed sensing
Computational efficiency
Convergence
Convex functions
Costs
Hyperbolic functions
Iterative algorithms
Noise
Phase transitions
Polynomials
Recovery
Regularization
Sensors
sparse recovery
Tensors
thresholding algorithm
Vectors
ISSN:1051-8215, 1558-2205
Online-Zugang:Volltext
Tags: Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
Beschreibung
Zusammenfassung:Several existing works have revealed the effectiveness of arctangent-type penalties in exploiting sparsity for compressed sensing. However, addressing the subproblems associated with the arctangent penalty incurs considerable computational cost. Aiming to reduce complexity, we derive the closed-form proximity operator of an arctangent penalty, which is expressed as hyperbolic functions of sine and cosine in this paper. Accordingly, a computationally-efficient arctangent regularization iterative thresholding (ARIT) algorithm for sparse approximation is proposed. Furthermore, we theoretically prove that under certain conditions, the ARIT algorithm converges to a local minimizer of the arctangent regularization problem with an eventually linear convergence. Extensive experiments are conducted to compare our scheme with conventional iterative thresholding algorithms, demonstrating the former superiority in terms of the probability of successful recovery, rate of support recovery, phase transition, and robustness to noise.
Bibliographie:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
ISSN:1051-8215
1558-2205
DOI:10.1109/TCSVT.2024.3524668