no code implementations • 31 Oct 2023 • Sergio Calvo-Ordonez, Chun-Wun Cheng, Jiahao Huang, Lipei Zhang, Guang Yang, Carola-Bibiane Schonlieb, Angelica I Aviles-Rivero
Diffusion Probabilistic Models stand as a critical tool in generative modelling, enabling the generation of complex data distributions.
no code implementations • 31 Mar 2023 • Jiahao Huang, Pedro F. Ferreira, Lichao Wang, Yinzhe Wu, Angelica I. Aviles-Rivero, Carola-Bibiane Schonlieb, Andrew D. Scott, Zohya Khalique, Maria Dwornik, Ramyah Rajakulasingam, Ranil De Silva, Dudley J. Pennell, Sonia Nielles-Vallespin, Guang Yang
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.
no code implementations • 23 Jan 2023 • Jiahao Huang, Angelica Aviles-Rivero, Carola-Bibiane Schonlieb, Guang Yang
The majority of existing deep learning models, e. g., convolutional neural networks, work on data with Euclidean or regular grids structures.
no code implementations • 17 Nov 2022 • Zhongying Deng, Yanqi Chen, Lihao Liu, Shujun Wang, Rihuan Ke, Carola-Bibiane Schonlieb, Angelica I Aviles-Rivero
Firstly, TrafficCAM provides both pixel-level and instance-level semantic labelling along with a large range of types of vehicles and pedestrians.
1 code implementation • 17 Nov 2022 • Zhongying Deng, Rihuan Ke, Carola-Bibiane Schonlieb, Angelica I Aviles-Rivero
Semi-Supervised Learning (SSL) aims to learn a model using a tiny labeled set and massive amounts of unlabeled data.
no code implementations • 8 Aug 2021 • Bogdan Toader, Jerome Boulanger, Yury Korolev, Martin O. Lenz, James Manton, Carola-Bibiane Schonlieb, Leila Muresan
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.
no code implementations • 10 Mar 2021 • Chao Li, Yiran Wei, Xi Chen, Carola-Bibiane Schonlieb
The proposed BrainNetGAN is a generative adversarial network variant to augment the brain structural connectivity matrices for binary dementia classification tasks.
no code implementations • 25 Oct 2018 • Angelica I. Aviles-Rivero, Noémie Debroux, Guy Williams, Martin J. Graves, Carola-Bibiane Schonlieb
Firstly, we propose a single optimisation problem that simultaneously computes the MRI reconstruction and the physical motion.
no code implementations • 26 Jul 2018 • Xiaohao Cai, Raymond Chan, Carola-Bibiane Schonlieb, Gabriele Steidl, Tieyong Zeng
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.
no code implementations • 24 Jan 2017 • Juheon Lee, David Coomes, Carola-Bibiane Schonlieb, Xiaohao Cai, Jan Lellmann, Michele Dalponte, Yadvinder Malhi, Nathalie Butt, Mike Morecroft
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).
no code implementations • 28 Jul 2014 • Juheon Lee, Xiaohao Cai, Carola-Bibiane Schonlieb, David Coomes
There is much current interest in using multi-sensor airborne remote sensing to monitor the structure and biodiversity of forests.