2012/08/31 by Rafael E. Carrillo, Jason D. McEwen, Dimitri Van De Ville +3 · 2 citations
Computer Science · Engineering · Mathematics · Physics and Astronomy · #Microwave Imaging and Scattering Analysis #Photoacoustic and Ultrasonic Imaging #Sparse and Compressive Sensing Techniques #astro-ph.IM #cs.IT #math.IT
paper · pdf · doi:10.1109/lsp.2013.2259813
published as IEEE Signal Processing Letters. Vol. 20, No. 6, 2013, pp 591-594 · 4 pages, 3 figures, accepted in IEEE signal processing letters
arxiv created 2013/04/16 · openalex publication_date 2013/04/24 · arxiv updated 2013/05/03 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/01
We discuss a novel sparsity prior for compressive imaging in the context of the theory of compressed sensing with coherent redundant dictionaries, based on the observation that natural images exhibit strong average sparsity over multiple coherent frames. We test our prior and the associated algorithm, based on an analysis reweighted formulation, through extensive numerical simulations on natural images for spread spectrum and random Gaussian acquisition schemes. Our results show that average sparsity outperforms state-of-the-art priors that promote sparsity in a single orthonormal basis or redundant frame, or that promote gradient sparsity. Code and test data are available at https://github.com/basp-group/sopt.