Robust Equivariant Imaging: a fully unsupervised framework for learning
to image from noisy and partial measurements

Robust Equivariant Imaging: a fully unsupervised framework for learning to image from noisy and partial measurements

25 November 2021

Julián Tachella

Mike E. Davies

Papers citing "Robust Equivariant Imaging: a fully unsupervised framework for learning to image from noisy and partial measurements"

8 / 8 papers shown

Title
Fully Unsupervised Dynamic MRI Reconstruction via Diffeo-Temporal Equivariance Andrew Wang Mike Davies MedIm 23 2 0 11 Oct 2024
Unsupervised Training of Convex Regularizers using Maximum Likelihood Estimation Hongwei Tan Ziruo Cai Marcelo Pereyra Subhadip Mukherjee Junqi Tang Carola-Bibiane Schönlieb SSL 55 1 0 08 Apr 2024
Ambient Diffusion Posterior Sampling: Solving Inverse Problems with Diffusion Models Trained on Corrupted Data Asad Aali Giannis Daras Brett Levac Sidharth Kumar Alexandros G. Dimakis Jonathan I. Tamir MedIm 54 9 0 13 Mar 2024
Rotational Augmented Noise2Inverse for Low-dose Computed Tomography Reconstruction Hang Xu A. Perelli 11 1 0 19 Dec 2023
What's in a Prior? Learned Proximal Networks for Inverse Problems Zhenghan Fang Sam Buchanan Jeremias Sulam 23 11 0 22 Oct 2023
Single-photon Image Super-resolution via Self-supervised Learning Yiwei Chen Chenbo Jiang Yu Pan 13 2 0 03 Mar 2023
Sensing Theorems for Unsupervised Learning in Linear Inverse Problems Julián Tachella Dongdong Chen Mike Davies SSL 20 22 0 23 Mar 2022
Rotationally Equivariant Super-Resolution of Velocity Fields in Two-Dimensional Fluids Using Convolutional Neural Networks Y. Yasuda R. Onishi 8 4 0 22 Feb 2022