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

Abstract

Deep networks provide state-of-the-art performance in multiple imaging inverse problems ranging from medical imaging to computational photography. However, most existing networks are trained with clean signals which are often hard or impossible to obtain. Equivariant imaging (EI) is a recent self-supervised learning framework that exploits the group invariance present in signal distributions to learn a reconstruction function from partial measurement data alone. While EI results are impressive, its performance degrades with increasing noise. In this paper, we propose a Robust Equivariant Imaging (REI) framework which can learn to image from noisy partial measurements alone. The proposed method uses Stein's Unbiased Risk Estimator (SURE) to obtain a fully unsupervised training loss that is robust to noise. We show that REI leads to considerable performance gains on linear and nonlinear inverse problems, thereby paving the way for robust unsupervised imaging with deep networks. Code is available at: https://github.com/edongdongchen/REI.

CVPR 2022. Code: https://github.com/edongdongchen/REI

References in corpus (7)

• Deep Image Prior
• fastMRI: An Open Dataset and Benchmarks for Accelerated MRI
• Generalized SURE for Exponential Families: Applications to Regularization
• On the Generalization of Equivariance and Convolution in Neural Networks to the Action of Compact Groups
• Near optimal compressed sensing without priors: Parametric SURE Approximate Message Passing
• Unsupervised Learning with Stein's Unbiased Risk Estimator
• Statistical Image Reconstruction Using Mixed Poisson-Gaussian Noise Model for X-Ray CT