2012/02/24 by Jiasong Wu, Xu Zhang, Wu, Jiasong +7
Computer Science · Engineering · #Blind Source Separation Techniques #Data Structures and Algorithms (cs.DS) #FOS: Computer and information sciences #FOS: Mathematics #G.1.3 #Image and Signal Denoising Methods #Information Theory (cs.IT) #Numerical Analysis (math.NA) #Sparse and Compressive Sensing Techniques
paper · pdf · doi:10.48550/arxiv.1202.5471
openalex publication_date 2012/02/24 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
The l1-norm minimization problem plays an important role in the compressed sensing (CS) theory. We present in this letter an algorithm for solving the problem of l1-norm minimization for quaternion signals by converting it to second-order cone programming. An application example of the proposed algorithm is also given for practical guidelines of perfect recovery of quaternion signals. The proposed algorithm may find its potential application when CS theory meets the quaternion signal processing.