43 datasets found

MRI reconstruction using data-driven Markov chains with Joint Uncertainty Estimation

Dynamic PET image reconstruction dataset

The Places dataset is a large-scale dataset for scene recognition, containing 1 million images from 365 categories.

The dataset used in the paper is not explicitly described, but it is mentioned that the authors tested their model on various signal reconstruction tasks: 1D sinusoidal...

The SHREC 2021 dataset contains 10 synthetic volumes with different distribution of proteins within "cells", named model 0 to model 9.

Physics-informed deep diffusion MRI reconstruction: Break the bottleneck of training data in artificial intelligence.

A paired phase and magnitude reconstruction for advanced diffusion-weighted imaging.

POCS-enhanced inherent correction of motion-induced phase errors (POCS-ICE) for high-resolution multishot diffusion MRI.

Fast and accurate reconstruction of magnetic resonance (MR) images from under-sampled data is important in many clinical applications. In recent years, deep learning-based...

CloudBrain-ReconAI is an online platform for MRI reconstruction and image quality evaluation.

Learning rich data representations from unlabeled data is a key challenge towards applying deep learning algorithms in downstream tasks. The proposed method learns sparse data...

Photoacoustic tomography (PAT) is a rapidly-evolving med-ical imaging modality that combines optical absorption contrast with ultrasound imaging depth. One challenge in PAT is...

The dataset used in the paper is a set of face images and cat images.

N-Caltech101 is a widely used event-based object recognition dataset.

N-ImageNet is a large-scale dataset containing 1,781,167 event stream data of 1,000 object classes.

Recognizing objects from sparse and noisy events becomes extremely difficult when paired images and category labels do not exist. This paper proposes a joint formulation of...

The dataset used in the paper is a synthetic dataset for blind super-resolution, consisting of low-resolution images and their corresponding blur kernels.

The dataset used for training and testing the proposed Incremental Residual Learning (IRL) framework.

Implicit neural representation, which expresses an image as a continuous function rather than a discrete grid form, is widely used for image processing. The dataset used in this...

The dataset used in the paper is a synthetic dataset for blind super-resolution, consisting of low-resolution images and their corresponding blur kernels.