246 citations · 472 across the 14 of their papers we have counts for
16 papers
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…
Spline Sketches: An Efficient Approach for Photon Counting Lidar
Michael Patrick Sheehan, Julian Tachella, Mike E. Davies
Photon counting lidar has become an invaluable tool for 3D depth imaging due to the fine-precision it can achieve over long ranges. However, high frame rate, high resolution lidar…
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…
Sketched RT3D: How to reconstruct billions of photons per second
Julián Tachella, Michael P. Sheehan, Mike E. Davies
Single-photon light detection and ranging (lidar) captures depth and intensity information of a 3D scene. Reconstructing a scene from observed photons is a challenging task due to…
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…
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…
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…
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…
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…
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…
Robust 3D reconstruction of dynamic scenes from single-photon lidar using Beta-divergences
Quentin Legros, Julian Tachella, Rachael Tobin +6
In this paper, we present a new algorithm for fast, online 3D reconstruction of dynamic scenes using times of arrival of photons recorded by single-photon detector arrays. One of t…
Seeing Around Corners with Edge-Resolved Transient Imaging
Joshua Rapp, Charles Saunders, Julián Tachella +7
Non-line-of-sight (NLOS) imaging is a rapidly growing field seeking to form images of objects outside the field of view, with potential applications in search and rescue, reconnais…
Real-time 3D reconstruction from single-photon lidar data using plug-and-play point cloud denoisers
Julián Tachella, Yoann Altmann, Nicolas Mellado +5
Single-photon lidar has emerged as a prime candidate technology for depth imaging through challenging environments. Until now, a major limitation has been the significant amount of…
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…
Bayesian 3D Reconstruction of Complex Scenes from Single-Photon Lidar Data
Julián Tachella, Yoann Altmann, Ximing Ren +4
Light detection and ranging (Lidar) data can be used to capture the depth and intensity profile of a 3D scene. This modality relies on constructing, for each pixel, a histogram of…