Year: 2022
Type: article
Abstract: Compressed sensing has captured considerable attention of researchers in the past decades. In this paper, with the aid of the powerful null space property, some deterministic recovery conditions are established for the previous $$\ell _{1}$$ ℓ 1 – $$\ell _{1}$$ ℓ 1 method and the $$\ell _{1}$$ ℓ 1 – $$\ell _{2}$$ ℓ 2 method to guarantee the exact sparse recovery when the side information of the desired signal is available. These obtained results provide a useful and necessary complement to the previous investigation of the $$\ell _{1}$$ ℓ 1 – $$\ell _{1}$$ ℓ 1 and $$\ell _{1}$$ ℓ 1 – $$\ell _{2}$$ ℓ 2 methods that are based on the statistical analysis. Moreover, one of our theoretical findings also shows that the sharp conditions previously established for the classical $$\ell _{1}$$ ℓ 1 method remain suitable for the $$\ell _{1}$$ ℓ 1 – $$\ell _{1}$$ ℓ 1 method to guarantee the exact sparse recovery. Numerical experiments on both the synthetic signals and the real-world images are also carried out to further test the recovery performance of the above two methods.
Authors: Xiaohu Luo, Nianci Feng, Xuhui Guo, Zili Zhang
Institutions: Southwest University, Ningxia University, Deakin University
Language: English
Subfield: Signal Processing
Field: Computer Science
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $1,665
OpenAlex ID: W4283662786