Regularization by architecture: A deep prior approach for inverse problems

10 December 2018

Papers citing "Regularization by architecture: A deep prior approach for inverse problems"

31 / 31 papers shown

Title
Optimization Landscapes Learned: Proxy Networks Boost Convergence in Physics-based Inverse Problems Girnar Goyal Philipp Holl Sweta Agrawal Nils Thuerey AI4CE 48 0 0 27 Jan 2025
Disentangled Representation Learning for Parametric Partial Differential Equations Ning Liu Lu Zhang Tian Gao Yue Yu DRL AI4CE 47 0 0 03 Oct 2024
Nonlocal Attention Operator: Materializing Hidden Knowledge Towards Interpretable Physics Discovery Yue Yu Ning Liu Fei Lu Tian Gao S. Jafarzadeh Stewart Silling AI4CE 48 7 0 14 Aug 2024
SDIP: Self-Reinforcement Deep Image Prior Framework for Image Processing Ziyu Shu Zhixin Pan 23 2 0 17 Apr 2024
Heterogeneous Peridynamic Neural Operators: Discover Biotissue Constitutive Law and Microstructure From Digital Image Correlation Measurements S. Jafarzadeh Stewart Silling Lu Zhang Colton J. Ross Chung-Hao Lee S. M. R. Rahman Shuodao Wang Yue Yu 28 5 0 27 Mar 2024
Robustness and Exploration of Variational and Machine Learning Approaches to Inverse Problems: An Overview Alexander Auras Kanchana Vaishnavi Gandikota Hannah Droege Michael Moeller AAML 31 0 0 19 Feb 2024
Weakly Convex Regularisers for Inverse Problems: Convergence of Critical Points and Primal-Dual Optimisation Zakhar Shumaylov Jeremy Budd Subhadip Mukherjee Carola-Bibiane Schönlieb 26 6 0 01 Feb 2024
An adaptively inexact first-order method for bilevel optimization with application to hyperparameter learning Mohammad Salehi Subhadip Mukherjee Lindon Roberts Matthias Joachim Ehrhardt 26 5 0 19 Aug 2023
SVD-DIP: Overcoming the Overfitting Problem in DIP-based CT Reconstruction Marco Nittscher Michael Lameter Riccardo Barbano Johannes Leuschner Bangti Jin Peter Maass 28 3 0 28 Mar 2023
Deep Learning Methods for Partial Differential Equations and Related Parameter Identification Problems Derick Nganyu Tanyu Jianfeng Ning Tom Freudenberg Nick Heilenkötter A. Rademacher U. Iben Peter Maass AI4CE 23 34 0 06 Dec 2022
K-UNN: k-Space Interpolation With Untrained Neural Network Zhuoxu Cui Seng Jia Qingyong Zhu Congcong Liu Zhilang Qiu Yuanyuan Liu Jing Cheng Haifeng Wang Yanjie Zhu Dong Liang 11 1 0 11 Aug 2022
CNN-based Euler's Elastica Inpainting with Deep Energy and Deep Image Prior Karl Schrader Tobias Alt Joachim Weickert M. Ertel 15 5 0 16 Jul 2022
An Educated Warm Start For Deep Image Prior-Based Micro CT Reconstruction Riccardo Barbano Johannes Leuschner Maximilian Schmidt Alexander Denker A. Hauptmann Peter Maass Bangti Jin 34 19 0 23 Nov 2021
Deformable image registration with deep network priors: a study on longitudinal PET images Constance Fourcade L. Ferrer Noémie Moreau Gianmarco Santini Aislinn Brennan ... M. Colombié P. Jézéquel Mario Campone M. Rubeaux Diana Mateus MedIm 11 4 0 22 Nov 2021
Survey of Deep Learning Methods for Inverse Problems S. Kamyab Zihreh Azimifar Rasool Sabzi Paul Fieguth 21 3 0 07 Nov 2021
Deep Bayesian inference for seismic imaging with tasks Ali Siahkoohi G. Rizzuti Felix J. Herrmann BDL UQCV 38 21 0 10 Oct 2021
Regularising Inverse Problems with Generative Machine Learning Models Margaret Duff Neill D. F. Campbell Matthias Joachim Ehrhardt GAN MedIm 29 34 0 22 Jul 2021
Unsupervised Knowledge-Transfer for Learned Image Reconstruction Riccardo Barbano Ž. Kereta A. Hauptmann Simon Arridge Bangti Jin 21 11 0 06 Jul 2021
Learning Regularization Parameters of Inverse Problems via Deep Neural Networks B. Afkham Julianne Chung Matthias Chung 17 42 0 14 Apr 2021
A Design Space Study for LISTA and Beyond Tianjian Meng Xiaohan Chen Yi Ding Zhangyang Wang 20 3 0 08 Apr 2021
Learned convex regularizers for inverse problems Subhadip Mukherjee Sören Dittmer Zakhar Shumaylov Sebastian Lunz Ozan Oktem Carola-Bibiane Schönlieb 25 79 0 06 Aug 2020
Deep Learning Methods for Solving Linear Inverse Problems: Research Directions and Paradigms Yanna Bai Wei Chen Jie Chen Weisi Guo 31 66 0 27 Jul 2020
Deep image prior for 3D magnetic particle imaging: A quantitative comparison of regularization techniques on Open MPI dataset Sören Dittmer T. Kluth Mads Thorstein Roar Henriksen Peter Maass 18 17 0 03 Jul 2020
Regularization of Inverse Problems by Neural Networks Markus Haltmeier Linh V. Nguyen 30 18 0 06 Jun 2020
Computed Tomography Reconstruction Using Deep Image Prior and Learned Reconstruction Methods Daniel Otero Baguer Johannes Leuschner Maximilian Schmidt 29 186 0 10 Mar 2020
Translating Diffusion, Wavelets, and Regularisation into Residual Networks Tobias Alt Joachim Weickert Pascal Peter DiffM 16 8 0 07 Feb 2020
Learned imaging with constraints and uncertainty quantification Felix J. Herrmann Ali Siahkoohi G. Rizzuti UQCV 22 23 0 13 Sep 2019
Neural reparameterization improves structural optimization Stephan Hoyer Jascha Narain Sohl-Dickstein S. Greydanus AI4CE 26 67 0 10 Sep 2019
A Projectional Ansatz to Reconstruction Sören Dittmer Peter Maass 14 3 0 10 Jul 2019
One-dimensional Deep Image Prior for Time Series Inverse Problems Sriram Ravula A. Dimakis 42 7 0 18 Apr 2019
Compressed Sensing with Deep Image Prior and Learned Regularization Dave Van Veen A. Jalal Mahdi Soltanolkotabi Eric Price S. Vishwanath A. Dimakis 33 178 0 17 Jun 2018