Volume 10, Issue 1 (4-2024)
mmr 2024, 10(1): 98-118 |
Back to browse issues page
Download citation:
BibTeX | RIS | EndNote | Medlars | ProCite | Reference Manager | RefWorks
Send citation to:
BibTeX | RIS | EndNote | Medlars | ProCite | Reference Manager | RefWorks
Send citation to:
Alamdari M S, Fatemi M, Ghaffari A. Applying sequential quadratic programming and smoothed L0 norm for recovery of noisy sparse signal. mmr 2024; 10 (1) :98-118
URL: http://mmr.khu.ac.ir/article-1-3330-en.html
URL: http://mmr.khu.ac.ir/article-1-3330-en.html
1- K. N. Toosi University of Technology , m.s.alamdari69@gmail.com
2- K. N. Toosi University of Technology
3- Iran University of Science and Technology
2- K. N. Toosi University of Technology
3- Iran University of Science and Technology
Abstract: (553 Views)
Sparse representation has many applications in signal and image processing, including applications in medical image reconstruction, image enhancement and compression, signal separation, array and radar signal processing. This importance has caused the researchers to benefit from a variety of sparse representation method and to use different norms to solve optimization problems. In this article, firstly, different methods of solving the sparse representation are reviewed, and then a new and efficient method is presented to recover noisy sparse signals with the benefit of sequential quadratic programming and smoothed L0 norm. The results of the experiments show the high success rate of the proposed method compared to other sparse representation optimization methods.
Keywords: Optimization, Sequential quadratic programming, Smoothed L0 norm, Sparse signal representation.
Type of Study: S |
Subject:
Mat
Received: 2023/05/4 | Revised: 2024/08/17 | Accepted: 2023/10/22 | Published: 2024/04/27 | ePublished: 2024/04/27
Received: 2023/05/4 | Revised: 2024/08/17 | Accepted: 2023/10/22 | Published: 2024/04/27 | ePublished: 2024/04/27
Send email to the article author
| Rights and permissions | |
 |
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License. |
