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Found 5 papers, 3 papers with code
SAMBA: A Trainable Segmentation Web-App with Smart Labelling
Segmentation is the assigning of a semantic class to every pixel in an image and is a prerequisite for various statistical analysis tasks in materials science, like phase quantification, physics simulations or morphological characterization.
HRTF upsampling with a generative adversarial network using a gnomonic equiangular projection
An individualised head-related transfer function (HRTF) is very important for creating realistic virtual reality (VR) and augmented reality (AR) environments.
Two approaches to inpainting microstructure with deep convolutional generative adversarial networks
Imaging is critical to the characterisation of materials.
MicroLib: A library of 3D microstructures generated from 2D micrographs using SliceGAN
3D microstructural datasets are commonly used to define the geometrical domains used in finite element modelling.
Fusion of complementary 2D and 3D mesostructural datasets using generative adversarial networks
In this paper, we present a method for combining information from pairs of distinct but complementary imaging techniques in order to accurately reconstruct the desired multi-phase, high resolution, representative, 3D images.
