2014/10/16 by Zai Yang, Lihua Xie, Yang, Zai +1
Computer Science · Engineering · Mathematics · #FOS: Computer and information sciences #Image and Signal Denoising Methods #Information Theory (cs.IT) #Sparse and Compressive Sensing Techniques #Structural Health Monitoring Techniques #cs.IT #math.IT
paper · pdf · doi:10.48550/arxiv.1410.4319
5 pages, 3 figures, submitted to ICASSP 2015, Brisbane, Australia, April 2015
arxiv created 2014/10/16 · openalex publication_date 2014/10/16 · arxiv updated 2014/10/17 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
The super-resolution theory developed recently by Candès and Fernandes-Granda aims to recover fine details of a sparse frequency spectrum from coarse scale information only. The theory was then extended to the cases with compressive samples and/or multiple measurement vectors. However, the existing atomic norm (or total variation norm) techniques succeed only if the frequencies are sufficiently separated, prohibiting commonly known high resolution. In this paper, a reweighted atomic-norm minimization (RAM) approach is proposed which iteratively carries out atomic norm minimization (ANM) with a sound reweighting strategy that enhances sparsity and resolution. It is demonstrated analytically and via numerical simulations that the proposed method achieves high resolution with application to DOA estimation.