353 citations · 1k across the 59 of their papers we have counts for
65 papers
Imaging with Equivariant Deep Learning
Dongdong Chen, Mike Davies, Matthias J. Ehrhardt +3
From early image processing to modern computational imaging, successful models and algorithms have relied on a fundamental property of natural signals: symmetry. Here symmetry refe…
Warm-Starting in Message Passing algorithms
Nikolajs Skuratovs, Mike E. Davies
Vector Approximate Message Passing (VAMP) provides the means of solving a linear inverse problem in a Bayes-optimal way assuming the measurement operator is sufficiently random. Ho…
Sensing Theorems for Unsupervised Learning in Linear Inverse Problems
Julián Tachella, Dongdong Chen, Mike Davies
Solving an ill-posed linear inverse problem requires knowledge about the underlying signal model. In many applications, this model is a priori unknown and has to be learned from da…
Adaptive Kernel Kalman Filter
Mengwei Sun, Mike E. Davies, Ian K. Proudler +1
Sequential Bayesian filters in non-linear dynamic systems require the recursive estimation of the predictive and posterior distributions. This paper introduces a Bayesian filter ca…
Unsupervised Learning From Incomplete Measurements for Inverse Problems
Julián Tachella, Dongdong Chen, Mike Davies
In many real-world inverse problems, only incomplete measurement data are available for training which can pose a problem for learning a reconstruction function. Indeed, unsupervis…
Robust Equivariant Imaging: a fully unsupervised framework for learning to image from noisy and partial measurements
Dongdong Chen, Julián Tachella, Mike E. Davies
Deep networks provide state-of-the-art performance in multiple imaging inverse problems ranging from medical imaging to computational photography. However, most existing networks a…