2019/03/23 by Yunyi Li, Fei Dai, Li, Yunyi +7 · 1 citation
Engineering · Medicine · #FOS: Electrical engineering #Image and Video Processing (eess.IV) #Microwave Imaging and Scattering Analysis #Photoacoustic and Ultrasonic Imaging #Sparse and Compressive Sensing Techniques #Ultrasound Imaging and Elastography #eess.IV #electronic engineering #information engineering
paper · pdf · doi:10.48550/arxiv.1903.09787
This is a raw manuscript, and all the authors agree to revise and improve it in the method, theory and experiments
openalex publication_date 2019/03/23 · arxiv created 2020/05/23 · arxiv updated 2020/05/26 · openalex created_date 2022/07/29 · openalex updated_date 2026/07/28
Group-prior based regularization method has led to great successes in various image processing tasks, which can usually be considered as a low-rank matrix minimization problem. As a widely used surrogate function of low-rank, the nuclear norm based convex surrogate usually lead to over-shrinking phenomena, since the nuclear norm shrinks the rank components (singular value) simultaneously. In this paper, we propose a novel Group-prior based nonconvex image compressive sensing (CS) reconstruction framework via a family of nonconvex nuclear norms functions which contain common concave and monotonically properties. To solve the resulting nonconvex nuclear norm minimization (NNM) problem, we develop a Group based iteratively double-reweighted nuclear norm algorithm (IDNN) via an alternating direction method of multipliers (ADMM) framework. Our proposed algorithm can convert the nonconvex nuclear norms optimization problem into a double-reweighted singular value thresholding (DSVT) problem. Extensive experiments demonstrate our proposed framework achieved favorable reconstruction performance compared with current state-of-the-art convex methods.