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Found 12 papers, 9 papers with code
Self-Supervised Learning for Image Super-Resolution and Deblurring
These methods critically rely on invariance to translations and/or rotations of the image distribution to learn from incomplete measurement data alone.
Learning to Reconstruct Signals From Binary Measurements
Here we explore the extreme case of learning from binary observations and provide necessary and sufficient conditions on the number of measurements required for identifying a set of signals from incomplete binary data.
Imaging with Equivariant Deep Learning
From early image processing to modern computational imaging, successful models and algorithms have relied on a fundamental property of natural signals: symmetry.
Sensing Theorems for Unsupervised Learning in Linear Inverse Problems
In this paper, we present necessary and sufficient sensing conditions for learning the signal model from measurement data alone which only depend on the dimension of the model and the number of operators or properties of the group action that the model is invariant to.
Sketched RT3D: How to reconstruct billions of photons per second
Single-photon light detection and ranging (lidar) captures depth and intensity information of a 3D scene.
Unsupervised Learning From Incomplete Measurements for Inverse Problems
In many real-world inverse problems, only incomplete measurement data are available for training which can pose a problem for learning a reconstruction function.
Robust Equivariant Imaging: a fully unsupervised framework for learning to image from noisy and partial measurements
Deep networks provide state-of-the-art performance in multiple imaging inverse problems ranging from medical imaging to computational photography.
Surface Detection for Sketched Single Photon Lidar
The computational load of the proposed detection algorithm depends solely on the size of the sketch, in contrast to previous algorithms that depend at least linearly in the number of collected photons or histogram bins, paving the way for fast, accurate and memory efficient lidar estimation.
Equivariant Imaging: Learning Beyond the Range Space
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 signals and associated measurements for training.
A Sketching Framework for Reduced Data Transfer in Photon Counting Lidar
Single-photon lidar has become a prominent tool for depth imaging in recent years.
The Neural Tangent Link Between CNN Denoisers and Non-Local Filters
While the NTK theory accurately predicts the filter associated with networks trained using standard gradient descent, our analysis shows that it falls short to explain the behaviour of networks trained using the popular Adam optimizer.
Seeing Around Corners with Edge-Resolved Transient Imaging
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, reconnaissance, and even medical imaging.
