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Found 11 papers, 1 papers with code
SHAMANN: Shared Memory Augmented Neural Networks
Current state-of-the-art methods for semantic segmentation use deep neural networks to learn the segmentation mask from the input image signal as an image-to-image mapping.
Hybrid adiabatic quantum computing for tomographic image reconstruction -- opportunities and limitations
Our goal is to reconstruct tomographic images with few measurements and a low signal-to-noise ratio.
SEMPAI: a Self-Enhancing Multi-Photon Artificial Intelligence for prior-informed assessment of muscle function and pathology
Deep learning (DL) shows notable success in biomedical studies.
Efficient and high accuracy 3-D OCT angiography motion correction in pathology
We propose a novel method for non-rigid 3-D motion correction of orthogonally raster-scanned optical coherence tomography angiography volumes.
Deep OCT Angiography Image Generation for Motion Artifact Suppression
The aim of this research is to fill these gaps using a deep generative model for OCT to OCTA image translation relying on a single intact OCT scan.
Fast and robust detection of solar modules in electroluminescence images
We compare our method to the state of the art and show that it is superior in presence of perspective distortion while the performance on images, where the module is roughly coplanar to the detector, is similar to the reference method.
Learning with Known Operators reduces Maximum Training Error Bounds
We assume that our analysis will support further investigation of known operators in other fields of physics, imaging, and signal processing.
PYRO-NN: Python Reconstruction Operators in Neural Networks
The high level Python API allows a simple use of the layers as known from Tensorflow.
Temporal and volumetric denoising via quantile sparse image prior
For the purpose of denoising, we propose a variational framework based on the QuaSI prior and a Huber data fidelity model that can handle 3-D and 3-D+t data.
Benchmarking Super-Resolution Algorithms on Real Data
Therefore, current studies are either evaluated 1) on simulated data or 2) on real data without a pixel-wise ground truth.
QuaSI: Quantile Sparse Image Prior for Spatio-Temporal Denoising of Retinal OCT Data
Optical coherence tomography (OCT) enables high-resolution and non-invasive 3D imaging of the human retina but is inherently impaired by speckle noise.
