This is the dataset for our paper "Multiscale fusion of digital rock images based on deep generative adversarial networks" submitted to GRL
This is the data set for experiments of satellite images of Oroville dam site in paper: Online Fusio...
The code and data for the paper "Deep learning segmentation of pore-scale structures by integrating ...
The purpose of image fusion is to combine the source images of the same scene into a single composit...
Low resolution and 2D high resolution input data, and instances of generated super resolution data f...
We image two altered rock samples consisting of a meta-igneous and a serpentinite showing an isolate...
A new method of reconstructing 3D tight sandstone digital rock with deep learning is proposed, and 3...
GANs (generative opposing networks) are a technique for learning deep representations in the absence...
For deep learning applications, the massive data development (e.g., collecting, labeling), which is ...
Deep learning is a branch of artificial intelligence (AI) focused on developing algorithms and model...
Digital Rock Physics (DRP) provides a fast way to compute rock properties and carry out a related se...
The survey paper summarizes the recent applications and developments in the domain of Generative Adv...
The article is an in-depth analysis of two leading approaches in the field of generative modeling: g...
Generative Adversarial Network is the topic of interest in today’s research in the field of image pr...
The practice of collecting data at a scene from multiple sensors of different types results in extre...
Datasets and codes for the paper "Hierarchical homogenization with deep-learning-based surrogate mod...
This is the data set for experiments of satellite images of Oroville dam site in paper: Online Fusio...
The code and data for the paper "Deep learning segmentation of pore-scale structures by integrating ...
The purpose of image fusion is to combine the source images of the same scene into a single composit...
Low resolution and 2D high resolution input data, and instances of generated super resolution data f...
We image two altered rock samples consisting of a meta-igneous and a serpentinite showing an isolate...
A new method of reconstructing 3D tight sandstone digital rock with deep learning is proposed, and 3...
GANs (generative opposing networks) are a technique for learning deep representations in the absence...
For deep learning applications, the massive data development (e.g., collecting, labeling), which is ...
Deep learning is a branch of artificial intelligence (AI) focused on developing algorithms and model...
Digital Rock Physics (DRP) provides a fast way to compute rock properties and carry out a related se...
The survey paper summarizes the recent applications and developments in the domain of Generative Adv...
The article is an in-depth analysis of two leading approaches in the field of generative modeling: g...
Generative Adversarial Network is the topic of interest in today’s research in the field of image pr...
The practice of collecting data at a scene from multiple sensors of different types results in extre...
Datasets and codes for the paper "Hierarchical homogenization with deep-learning-based surrogate mod...
This is the data set for experiments of satellite images of Oroville dam site in paper: Online Fusio...
The code and data for the paper "Deep learning segmentation of pore-scale structures by integrating ...
The purpose of image fusion is to combine the source images of the same scene into a single composit...
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