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Handbook of Mathematical Models and Algorithms in Computer Vision and Imaging
Juan Carlos De los Reyes and David Villacís Handbook of Mathematical Models and Algorithms in Computer Vision and Imaging 909 (2023) https://doi.org/10.1007/978-3-030-98661-2_66
Dualization and Automatic Distributed Parameter Selection of Total Generalized Variation via Bilevel Optimization
Michael Hintermüller, Kostas Papafitsoros, Carlos N. Rautenberg and Hongpeng Sun Numerical Functional Analysis and Optimization 43(8) 887 (2022) https://doi.org/10.1080/01630563.2022.2069812
Optimality Conditions for Bilevel Imaging Learning Problems with Total Variation Regularization
Research Note: Recovering sparse models in 3D potential‐field inversion without bound dependence or staircasing problems using a mixed Lp norm regularization
Characterization of Spatially Graded Biomechanical Scaffolds
Nicholas R. Hugenberg, Li Dong, James A. Cooper, David T. Corr and Assad A. Oberai Journal of Biomechanical Engineering 142(7) (2020) https://doi.org/10.1115/1.4045905
Tube-Based Taut String Algorithms for Total Variation Regularization
Image Restoration by Combined Order Regularization with Optimal Spatial Adaptation
Sanjay Viswanath, Manu Ghulyani, Simon De Beco, Maxime Dahan and Muthuvel Arigovindan IEEE Transactions on Image Processing 1 (2020) https://doi.org/10.1109/TIP.2020.2988146
Processing, Analyzing and Learning of Images, Shapes, and Forms: Part 2
Michael Hintermüller and Kostas Papafitsoros Handbook of Numerical Analysis, Processing, Analyzing and Learning of Images, Shapes, and Forms: Part 2 20 437 (2019) https://doi.org/10.1016/bs.hna.2019.08.001
Convergence rates and structure of solutions of inverse problems with imperfect forward models
Scale Space and Variational Methods in Computer Vision
Martin Burger, Yury Korolev, Carola-Bibiane Schönlieb and Christiane Stollenwerk Lecture Notes in Computer Science, Scale Space and Variational Methods in Computer Vision 11603 485 (2019) https://doi.org/10.1007/978-3-030-22368-7_38
Analysis and automatic parameter selection of a variational model for mixed Gaussian and salt-and-pepper noise removal
Analytical aspects of spatially adapted total variation regularisation
Michael Hintermüller, Konstantinos Papafitsoros and Carlos N. Rautenberg Journal of Mathematical Analysis and Applications 454(2) 891 (2017) https://doi.org/10.1016/j.jmaa.2017.05.025
A fractional-order derivative based variational framework for image denoising
Infimal Convolution Regularisation Functionals of BV and $$\varvec{\mathrm {L}}^{\varvec{p}}$$ L p Spaces
Martin Burger, Konstantinos Papafitsoros, Evangelos Papoutsellis and Carola-Bibiane Schönlieb Journal of Mathematical Imaging and Vision 55(3) 343 (2016) https://doi.org/10.1007/s10851-015-0624-6
Big Data Optimization: Recent Developments and Challenges
Scale Space and Variational Methods in Computer Vision
Eva-Maria Brinkmann, Martin Burger and Joana Grah Lecture Notes in Computer Science, Scale Space and Variational Methods in Computer Vision 9087 191 (2015) https://doi.org/10.1007/978-3-319-18461-6_16
X‐ray computed tomography using curvelet sparse regularization
Matthias Wieczorek, Jürgen Frikel, Jakob Vogel, Elena Eggl, Felix Kopp, Peter B. Noël, Franz Pfeiffer, Laurent Demaret and Tobias Lasser Medical Physics 42(4) 1555 (2015) https://doi.org/10.1118/1.4914368
A study of the one dimensional total generalised variation regularisation problem
Jan Lellmann, Konstantinos Papafitsoros, Carola Schönlieb and Daniel Spector SIAM Journal on Imaging Sciences 8(4) 2161 (2015) https://doi.org/10.1137/140993818
The Jump Set under Geometric Regularization. Part 1: Basic Technique and First-Order Denoising
Efficient Algorithms for Global Optimization Methods in Computer Vision
Kristian Bredies Lecture Notes in Computer Science, Efficient Algorithms for Global Optimization Methods in Computer Vision 8293 44 (2014) https://doi.org/10.1007/978-3-642-54774-4_3
Level Set and PDE Based Reconstruction Methods in Imaging
Martin Burger and Stanley Osher Lecture Notes in Mathematics, Level Set and PDE Based Reconstruction Methods in Imaging 2090 1 (2013) https://doi.org/10.1007/978-3-319-01712-9_1
Posterior Expectation of the Total Variation Model: Properties and Experiments
On behavior of signs for the heat equation and a diffusion method for data separation
Noriaki Umeda, Takeshi Ohtsuka, Yoshikazu Giga and Mi-Ho Giga Communications on Pure and Applied Analysis 12(5) 2277 (2013) https://doi.org/10.3934/cpaa.2013.12.2277
A full second order variational model for multiscale texture analysis
Local behavior of sparse analysis regularization: Applications to risk estimation
Samuel Vaiter, Charles-Alban Deledalle, Gabriel Peyré, Charles Dossal and Jalal Fadili Applied and Computational Harmonic Analysis 35(3) 433 (2013) https://doi.org/10.1016/j.acha.2012.11.006
Near-Optimal Compressed Sensing Guarantees for Total Variation Minimization
Computer Vision, Imaging and Computer Graphics. Theory and Application
Kristian Bredies and Martin Holler Communications in Computer and Information Science, Computer Vision, Imaging and Computer Graphics. Theory and Application 359 242 (2013) https://doi.org/10.1007/978-3-642-38241-3_16
Robust Sparse Analysis Regularization
Samuel Vaiter, Gabriel Peyre, Charles Dossal and Jalal Fadili IEEE Transactions on Information Theory 59(4) 2001 (2013) https://doi.org/10.1109/TIT.2012.2233859
Image restoration: Total variation, wavelet frames, and beyond