Search Results for author:
Found 23 papers, 5 papers with code
Variational multichannel multiclass segmentation\endgraf using unsupervised lifting with CNNs
We propose an unsupervised image segmentation approach, that combines a variational energy functional and deep convolutional neural networks.
Unsupervised Joint Image Transfer and Uncertainty Quantification Using Patch Invariant Networks
To ensure a structure-preserving mapping from the input to the target domain, existing methods for unpaired image transfer are commonly based on cycle-consistency, causing additional computational resources and instability due to the learning of an inverse mapping.
Convolutional Dictionary Learning by End-To-End Training of Iterative Neural Networks
Sparsity-based methods have a long history in the field of signal processing and have been successfully applied to various image reconstruction problems.
Convolutional Analysis Operator Learning by End-To-End Training of Iterative Neural Networks
Iterative neural networks - which contain the physical model - can overcome these issues.
Feature reconstruction from incomplete tomographic data without detour
In this paper, we consider the problem of feature reconstruction from incomplete x-ray CT data.
A Joint Variational Multichannel Multiphase Segmentation Framework
In this paper, we propose a variational image segmentation framework for multichannel multiphase image segmentation based on the Chan-Vese active contour model.
Unsupervised Single-shot Depth Estimation using Perceptual Reconstruction
Real-time estimation of actual object depth is an essential module for various autonomous system tasks such as 3D reconstruction, scene understanding and condition assessment.
Unpaired Single-Image Depth Synthesis with cycle-consistent Wasserstein GANs
Real-time estimation of actual environment depth is an essential module for various autonomous system tasks such as localization, obstacle detection and pose estimation.
Surface Topography Characterization Using a Simple Optical Device and Artificial Neural Networks
A novel machine learning framework is proposed that allows prediction of the bearing load curves from RGB images of the liner surface that can be collected with a handheld microscope.
An End-To-End-Trainable Iterative Network Architecture for Accelerated Radial Multi-Coil 2D Cine MR Image Reconstruction
The network is based on a computationally light CNN-component and a subsequent conjugate gradient (CG) method which can be jointly trained end-to-end using an efficient training strategy.
Deep Structure Learning using Feature Extraction in Trained Projection Space
Over the last decade of machine learning, convolutional neural networks have been the most striking successes for feature extraction of rich sensory and high-dimensional data.
Regularization of Inverse Problems by Neural Networks
Inverse problems arise in a variety of imaging applications including computed tomography, non-destructive testing, and remote sensing.
Sparse aNETT for Solving Inverse Problems with Deep Learning
We propose a sparse reconstruction framework (aNETT) for solving inverse problems.
Unsupervised Adaptive Neural Network Regularization for Accelerated Radial Cine MRI
We compare the proposed reconstruction scheme to two ground truth-free reconstruction methods, namely a well known Total Variation (TV) minimization and an unsupervised adaptive Dictionary Learning (DIC) method.
Deep synthesis regularization of inverse problems
For these deep learning methods, however, a solid theoretical foundation in the form of reconstruction guarantees is missing.
Neural Networks-based Regularization for Large-Scale Medical Image Reconstruction
In this paper we present a generalized Deep Learning-based approach for solving ill-posed large-scale inverse problems occuring in medical image reconstruction.
Random 2.5D U-net for Fully 3D Segmentation
Convolutional neural networks are state-of-the-art for various segmentation tasks.
Augmented NETT Regularization of Inverse Problems
We propose aNETT (augmented NETwork Tikhonov) regularization as a novel data-driven reconstruction framework for solving inverse problems.
A Joint Deep Learning Approach for Automated Liver and Tumor Segmentation
Hepatocellular carcinoma (HCC) is the most common type of primary liver cancer in adults, and the most common cause of death of people suffering from cirrhosis.
Projection-Based 2.5D U-net Architecture for Fast Volumetric Segmentation
While for 2D images these networks are also computationally efficient, 3D convolutions have huge storage requirements and require long training time.
Image Based Fashion Product Recommendation with Deep Learning
We develop a two-stage deep learning framework that recommends fashion images based on other input images of similar style.
NETT: Solving Inverse Problems with Deep Neural Networks
Our theoretical results and framework are different from any previous work using neural networks for solving inverse problems.
Deep Learning for Photoacoustic Tomography from Sparse Data
In our approach image reconstruction is performed with a deep convolutional neural network (CNN), whose weights are adjusted prior to the actual image reconstruction based on a set of training data.
