246 citations · 472 across the 14 of their papers we have counts for
3 papers · 1 filter
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…
Equivariant Imaging: Learning Beyond the Range Space
Dongdong Chen, Julián Tachella, Mike E. Davies
In various imaging problems, we only have access to compressed measurements of the underlying signals, hindering most learning-based strategies which usually require pairs of signa…
The Neural Tangent Link Between CNN Denoisers and Non-Local Filters
Julián Tachella, Junqi Tang, Mike Davies
Convolutional Neural Networks (CNNs) are now a well-established tool for solving computational imaging problems. Modern CNN-based algorithms obtain state-of-the-art performance in…