Joint L1/Lp-regularized minimization in video recovery of remote sensing based on compressed sensing

Autor:	Long-long Xiao, Sheng-liang Li, Kun Liu, Feng Zhang, Da-peng Han
Rok vydání:	2014
Předmět:	Compressed sensing Computer science Convex optimization Regularization perspectives on support vector machines Minification Electrical and Electronic Engineering Regularization (mathematics) Atomic and Molecular Physics and Optics Electronic Optical and Magnetic Materials Remote sensing
Zdroj:	Optik. 125:7080-7084
ISSN:	0030-4026
DOI:	10.1016/j.ijleo.2014.08.098
Popis:	L 1 regularization and L p regularization are proposed for processing recovered images based on compressed sensing (CS). L 1 regularization can be solved as a convex optimization problem but is less sparse than L p (0 p L p regularization is sparser than L 1 regularization but is more difficult to solve. This paper proposes joint L 1 / L p (0 p L p regularization and L 1 regularization. This joint regularization is applied to recover video of remote sensing based on CS. Joint regularization is sparser than L 1 regularization but is as easy to solve as L 1 regularization. A linearized Bregman reweighted iteration algorithm is proposed to solve the joint L 1 / L p regularization problem. The performance and capabilities of the linearized Bregman algorithm and linearized Bregman reweighted algorithm for solving the joint L 1 / L p regularization model are analyzed and compared through numerical simulations.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_________::17298422b32d738a719c28176e28d432 https://doi.org/10.1016/j.ijleo.2014.08.098 Zobrazit plný text záznamu Full Text from ScienceDirect