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Found 11 papers, 1 papers with code
The Missing U for Efficient Diffusion Models
Diffusion Probabilistic Models stand as a critical tool in generative modelling, enabling the generation of complex data distributions.
Deep Learning-based Diffusion Tensor Cardiac Magnetic Resonance Reconstruction: A Comparison Study
Our results indicate that the models we discussed in this study can be applied for clinical use at an acceleration factor (AF) of $\times 2$ and $\times 4$, with the D5C5 model showing superior fidelity for reconstruction and the SwinMR model providing higher perceptual scores.
ViGU: Vision GNN U-Net for Fast MRI
The majority of existing deep learning models, e. g., convolutional neural networks, work on data with Euclidean or regular grids structures.
TrafficCAM: A Versatile Dataset for Traffic Flow Segmentation
Firstly, TrafficCAM provides both pixel-level and instance-level semantic labelling along with a large range of types of vehicles and pedestrians.
NorMatch: Matching Normalizing Flows with Discriminative Classifiers for Semi-Supervised Learning
Semi-Supervised Learning (SSL) aims to learn a model using a tiny labeled set and massive amounts of unlabeled data.
Image reconstruction in light-sheet microscopy: spatially varying deconvolution and mixed noise
Then, we formulate a variational model that accounts for the combination of Poisson and Gaussian noise through a data fidelity term consisting of the infimal convolution of the single noise fidelities, first introduced in L. Calatroni et al. "Infimal convolution of data discrepancies for mixed noise removal", SIAM Journal on Imaging Sciences 10. 3 (2017), 1196-1233.
BrainNetGAN: Data augmentation of brain connectivity using generative adversarial network for dementia classification
The proposed BrainNetGAN is a generative adversarial network variant to augment the brain structural connectivity matrices for binary dementia classification tasks.
Compressed Sensing Plus Motion (CS+M): A New Perspective for Improving Undersampled MR Image Reconstruction
Firstly, we propose a single optimisation problem that simultaneously computes the MRI reconstruction and the physical motion.
Linkage between piecewise constant Mumford-Shah model and ROF model and its virtue in image segmentation
The piecewise constant Mumford-Shah (PCMS) model and the Rudin-Osher-Fatemi (ROF) model are two important variational models in image segmentation and image restoration, respectively.
A graph cut approach to 3D tree delineation, using integrated airborne LiDAR and hyperspectral imagery
Here we develop a 3D tree delineation method which uses graph cut to delineate trees from the full 3D LiDAR point cloud, and also makes use of any optical imagery available (hyperspectral imagery in our case).
Non-parametric Image Registration of Airborne LiDAR, Hyperspectral and Photographic Imagery of Forests
There is much current interest in using multi-sensor airborne remote sensing to monitor the structure and biodiversity of forests.
