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FAIR: Frequency-aware Image Restoration for Industrial Visual Anomaly Detection
Image reconstruction-based anomaly detection models are widely explored in industrial visual inspection. However, existing models usually suffer from the trade-off between... -
CSI2Image: Image Reconstruction from Channel State Information Using Generati...
This study proposes CSI2Image, a novel channel-state-information (CSI)-to-image conversion method based on generative adversarial networks (GANs). -
Optimization methods for MR image reconstruction
The dataset used in this paper for optimization methods for MR image reconstruction -
Optical Diffraction Tomography Dataset
The dataset used in this paper for optical diffraction tomography. -
Phase Retrieval and Optical Diffraction Tomography Datasets
The dataset used in this paper for phase retrieval and optical diffraction tomography. -
Compressed Sensing Dataset
The dataset used in the paper is a compressed sensing problem – under-determined sparse recovery from linear Gaussian random measurements. -
Real preclinical data from a mouse injected with GNP
Real preclinical data from a mouse injected with GNP -
Simulated clinical images
Simulated clinical images -
Fourth-order nonlocal tensor decomposition model for spectral CT image recons...
Fourth-order nonlocal tensor decomposition model for spectral CT image reconstruction -
LoDoPaB-CT
LoDoPaB-CT -
AAPM Challenge Data and CQ500 dataset
Low-dose CT imaging dataset using AAPM Challenge Data and CQ500 dataset -
CIRS Phantom Dataset
A high-quality CT volume dataset for testing purposes. -
Low Dose CT Grand Challenge
A dataset for low-dose CT image reconstruction. -
Stacked Wasserstein Autoencoder
The proposed model is built on the theoretical analysis presented in [30,14]. Similar to the ARAE [14], our model provides flexibility in learning an autoencoder from the input... -
Data Science Bowl challenge dataset
Compressed Sensing MRI reconstruction using a Generative Adversarial Network with a Cyclic Loss -
Sparse-view CT reconstruction dataset
Sparse-view CT reconstruction dataset using AAPM Challenge Data and CIRS Phantom Data -
NIH-AAPM-Mayo Clinic Low-Dose CT Grand Challenge dataset
Sparse-view computed tomography (CT) can be used to reduce radiation dose greatly but is suffers from severe image artifacts. -
DIS-D dataset
This dataset has no description
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MNIST, CIFAR10, and UDIS-D datasets
The MNIST and CIFAR10 datasets are used for image classification, while the UDIS-D dataset is used for image reconstruction.
