31 citations · 82 across the 17 of their papers we have counts for
11 papers · 1 filter
Differentiable Sensor Layouts for End-to-End Learning of Task-Specific Camera Parameters
Hendrik Sommerhoff, Shashank Agnihotri, Mohamed Saleh +3
The success of deep learning is frequently described as the ability to train all parameters of a network on a specific application in an end-to-end fashion. Yet, several design cho…
Neural Volume Super-Resolution
Yuval Bahat, Yuxuan Zhang, Hendrik Sommerhoff +2
Neural volumetric representations have become a widely adopted model for radiance fields in 3D scenes. These representations are fully implicit or hybrid function approximators of…
Light Field Implicit Representation for Flexible Resolution Reconstruction
Paramanand Chandramouli, Hendrik Sommerhoff, Andreas Kolb
Inspired by the recent advances in implicitly representing signals with trained neural networks, we aim to learn a continuous representation for narrow-baseline 4D light fields. We…
Training Auto-encoder-based Optimizers for Terahertz Image Reconstruction
Tak Ming Wong, Matthias Kahl, Peter Haring Bolívar +2
Terahertz (THz) sensing is a promising imaging technology for a wide variety of different applications. Extracting the interpretable and physically meaningful parameters for such a…
Supervised Deep Kriging for Single-Image Super-Resolution
Gianni Franchi, Angela Yao, Andreas Kolb
We propose a novel single-image super-resolution approach based on the geostatistical method of kriging. Kriging is a zero-bias minimum-variance estimator that performs spatial int…
A Bit Too Much? High Speed Imaging from Sparse Photon Counts
Paramanand Chandramouli, Samuel Burri, Claudio Bruschini +2
Recent advances in photographic sensing technologies have made it possible to achieve light detection in terms of a single photon. Photon counting sensors are being increasingly us…