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Scale Space and Variational Methods in Computer Vision

9th International Conference, SSVM 2023, Santa Margherita di Pula, Italy, May 21–25, 2023, Proceedings

  • Conference proceedings
  • © 2023

Overview

Editors:
  1. Luca Calatroni

    1. CNRS, Université Côte d'Azur, Sophia-Antipolis, France

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  2. Marco Donatelli

    1. University of Insubria, Como, Italy

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  3. Serena Morigi

    1. University of Bologna, Bologna, Italy

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  4. Marco Prato

    1. University of Modena and Reggio Emilia, Modena, Italy

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  5. Matteo Santacesaria

    1. University of Genova, Genova, Italy

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Part of the book series: Lecture Notes in Computer Science (LNCS, volume 14009)

Included in the following conference series:

Conference proceedings info: SSVM 2023.

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Table of contents (57 papers)

  1. Front Matter

    Pages i-xvi
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  2. Inverse Problems in Imaging

    1. Front Matter

      Pages 1-1
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    2. Explicit Diffusion of Gaussian Mixture Model Based Image Priors

      • Martin Zach, Thomas Pock, Erich Kobler, Antonin Chambolle
      Pages 3-15

    3. Efficient Neural Generation of 4K Masks for Homogeneous Diffusion Inpainting

      • Karl Schrader, Pascal Peter, Niklas Kämper, Joachim Weickert
      Pages 16-28

    4. Theoretical Foundations for Pseudo-Inversion of Nonlinear Operators

      • Eyal Gofer, Guy Gilboa
      Pages 29-41

    5. A Frame Decomposition of the Funk-Radon Transform

      • Michael Quellmalz, Lukas Weissinger, Simon Hubmer, Paul D. Erchinger
      Pages 42-54

    6. Prony-Based Super-Resolution Phase Retrieval of Sparse, Multidimensional Signals

      • Robert Beinert, Saghar Rezaei
      Pages 55-67

    7. Limited Electrodes Models in Electrical Impedance Tomography Reconstruction

      • Francesco Colibazzi, Damiana Lazzaro, Serena Morigi, Andrea Samorè
      Pages 68-80

    8. On Trainable Multiplicative Noise Removal Models

      • Mahipal Jetta, Utkarsh Singh, Padmaja Yinukula
      Pages 81-93

    9. Surface Reconstruction from Noisy Point Cloud Using Directional G-norm

      • Guangyu Cui, Ho Law, Sung Ha Kang
      Pages 94-106

    10. Regularized Material Decomposition for K-edge Separation in Hyperspectral Computed Tomography

      • Francesca Bevilacqua, Yiqiu Dong, Jakob Sauer Jørgensen
      Pages 107-119

    11. Quaternary Image Decomposition with Cross-Correlation-Based Multi-parameter Selection

      • Laura Girometti, Martin Huska, Alessandro Lanza, Serena Morigi
      Pages 120-133

  3. Machine and Deep Learning in Imaging

    1. Front Matter

      Pages 135-135
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    2. EmNeF: Neural Fields for Embedded Variational Problems in Imaging

      • Danielle Bednarski, Jan Lellmann
      Pages 137-148

    3. GenHarris-ResNet: A Rotation Invariant Neural Network Based on Elementary Symmetric Polynomials

      • Valentin Penaud--Polge, Santiago Velasco-Forero, Jesus Angulo
      Pages 149-161

    4. Compressive Learning of Deep Regularization for Denoising

      • Hui Shi, Yann Traonmilin, Jean-François Aujol
      Pages 162-174

    5. Graph Laplacian and Neural Networks for Inverse Problems in Imaging: GraphLaNet

      • Davide Bianchi, Marco Donatelli, Davide Evangelista, Wenbin Li, Elena Loli Piccolomini
      Pages 175-186

    6. Learning Posterior Distributions in Underdetermined Inverse Problems

      • Christina Runkel, Michael Moeller, Carola-Bibiane Schönlieb, Christian Etmann
      Pages 187-209

    7. Proximal Residual Flows for Bayesian Inverse Problems

      • Johannes Hertrich
      Pages 210-222

    8. A Model is Worth Tens of Thousands of Examples

      • Thomas Dagès, Laurent D. Cohen, Alfred M. Bruckstein
      Pages 223-235
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  1. Scale Space and Variational Methods in Computer Vision

Keywords

About this book

This book constitutes the proceedings of the 9th International Conference on Scale Space and Variational Methods in Computer Vision, SSVM 2023, which took place in Santa Margherita di Pula, Italy, in May 2023. 


The 57 papers presented in this volume were carefully reviewed and selected from 72 submissions. They were organized in topical sections as follows: Inverse Problems in Imaging; Machine and Deep Learning in Imaging; Optimization for Imaging: Theory and Methods; Scale Space, PDEs, Flow, Motion and Registration.

Editors and Affiliations

  • CNRS, Université Côte d'Azur, Sophia-Antipolis, France

    Luca Calatroni

  • University of Insubria, Como, Italy

    Marco Donatelli

  • University of Bologna, Bologna, Italy

    Serena Morigi

  • University of Modena and Reggio Emilia, Modena, Italy

    Marco Prato

  • University of Genova, Genova, Italy

    Matteo Santacesaria

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