2015/07/31 by Myung Cho, Kumar Vijay Mishra, Jian‐Feng Cai +2
Computer Science · Engineering · Mathematics · #Algorithm #Artificial intelligence #Block (permutation group theory) #Compressed sensing #Computer science #Heuristic #Image and Signal Denoising Methods #Iterative method #Mathematics #Minification #Photoacoustic and Ultrasonic Imaging #Sparse and Compressive Sensing Techniques #cs.IT #math.IT
paper · pdf · doi:10.1109/lsp.2015.2478854
arxiv created 2015/09/11 · openalex publication_date 2015/09/15 · arxiv updated 2015/10/28 · openalex created_date 2016/06/24 · openalex updated_date 2026/08/05
We propose novel algorithms that enhance the performance of recovering unknown continuous-valued frequencies from undersampled signals. Our iterative reweighted frequency recovery algorithms employ the support knowledge gained from earlier steps of our algorithms as block prior information to enhance frequency recovery. Our methods improve the performance of the atomic norm minimization which is a useful heuristic in recovering continuous-valued frequency contents. Numerical results demonstrate that our block iterative reweighted methods provide both better recovery performance and faster speed than other known methods.