Linear inverse problems with Hessian-Schatten total variation

被引:5
作者
Ambrosio, Luigi [1 ]
Aziznejad, Shayan [2 ]
Brena, Camillo [1 ]
Unser, Michael [2 ]
机构
[1] Scuola Normale Super Pisa, Pisa, Italy
[2] Ecole Polytech Fed Lausanne, Biomed Imaging Grp, Lausanne, Switzerland
基金
欧洲研究理事会;
关键词
REGULARIZATION; RECONSTRUCTION; EQUATIONS; NETWORKS; SYSTEMS; SPACE;
D O I
10.1007/s00526-023-02611-6
中图分类号
O29 [应用数学];
学科分类号
070104 ;
摘要
In this paper, we characterize the class of extremal points of the unit ball of the Hessian-Schatten total variation (HTV) functional. The underlying motivation for our work stems from a general representer theorem that characterizes the solution set of regularized linear inverse problems in terms of the extremal points of the regularization ball. Our analysis is mainly based on studying the class of continuous and piecewise linear (CPWL) functions. In particular, we show that in dimension d = 2, CPWL functions are dense in the unit ball of the HTV functional. Moreover, we prove that a CPWL function is extremal if and only if its Hessian is minimally supported. For the converse, we prove that the density result (which we have only proven for dimension d = 2) implies that the closure of the CPWL extreme points contains all extremal points.
引用
收藏
页数:28
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  • [1] RANK ONE PROPERTY FOR DERIVATIVES OF FUNCTIONS WITH BOUNDED VARIATION
    ALBERTI, G
    [J]. PROCEEDINGS OF THE ROYAL SOCIETY OF EDINBURGH SECTION A-MATHEMATICS, 1993, 123 : 239 - 274
  • [2] Ambrosio L., 2000, OX MATH M, pxviii, DOI 10.1017/S0024609301309281
  • [3] Ambrosio L, 2023, Arxiv, DOI arXiv:2302.12554
  • [4] Arora R., 2018, INT C LEARN REPR, P1
  • [5] Aziznejad S., 2023, arXiv
  • [6] Sparsest Univariate Learning Models Under Lipschitz Constraint
    Aziznejad, Shayan
    Debarre, Thomas
    Unser, Michael
    [J]. IEEE OPEN JOURNAL OF SIGNAL PROCESSING, 2022, 3 : 140 - 154
  • [7] Duality Mapping for Schatten Matrix Norms
    Aziznejad, Shayan
    Unser, Michael
    [J]. NUMERICAL FUNCTIONAL ANALYSIS AND OPTIMIZATION, 2021, 42 (06) : 679 - 695
  • [8] Deep Neural Networks With Trainable Activations and Controlled Lipschitz Constant
    Aziznejad, Shayan
    Gupta, Harshit
    Campos, Joaquim
    Unser, Michael
    [J]. IEEE TRANSACTIONS ON SIGNAL PROCESSING, 2020, 68 : 4688 - 4699
  • [9] A Second-Order Model for Image Denoising
    Bergounioux, Maitine
    Piffet, Loic
    [J]. SET-VALUED AND VARIATIONAL ANALYSIS, 2010, 18 (3-4) : 277 - 306
  • [10] Bhatia R., 1997, Matrix Analysis, DOI DOI 10.1007/978-1-4612-0653-8