246 citations · 472 across the 14 of their papers we have counts for
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Learning to Reconstruct Signals From Binary Measurements
Julián Tachella, Laurent Jacques
Recent advances in unsupervised learning have highlighted the possibility of learning to reconstruct signals from noisy and incomplete linear measurements alone. These methods play…
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…
Surface Detection for Sketched Single Photon Lidar
Michael P. Sheehan, Julián Tachella, Mike E. Davies
Single-photon lidar devices are able to collect an ever-increasing amount of time-stamped photons in small time periods due to increasingly larger arrays, generating a memory and c…
A Sketching Framework for Reduced Data Transfer in Photon Counting Lidar
Michael P. Sheehan, Julián Tachella, Mike E. Davies
Single-photon lidar has become a prominent tool for depth imaging in recent years. At the core of the technique, the depth of a target is measured by constructing a histogram of ti…
Bayesian 3D Reconstruction of Subsampled Multispectral Single-photon Lidar Signals
Julián Tachella, Yoann Altmann, Miguel Márquez +3
Light detection and ranging (Lidar) single-photon devices capture range and intensity information from a 3D scene. This modality enables long range 3D reconstruction with high rang…