scholar.google.com › citations
Volume upscaling generates high-resolution volumes from low-resolution volumes to make data exploration more effective. Traditional methods, such as the ...
Volume upscaling generates high-resolution volumes from low-resolution volumes to make data exploration more effective. Traditional methods, such as the ...
Jun 30, 2017 · ABSTRACT. Volume upscaling generates high-resolution volumes from low-resolution volumes to make data exploration more ef- fective.
Nov 29, 2020 · Convolutional neural networks are Deep Learning algorithms commonly used in image recognition and natural language processing.
Missing: Volume | Show results with:Volume
Jan 15, 2022 · This study has disclosed an upscaling method taking advantage of convolutional neural networks (CNNs) and downsampling techniques.
We propose a lightweight convolutional neural network (CNN)-based algorithm which simultaneously performs artifacts reduction and super resolution (ARSR)
The first stage initializes a low-resolution vector field based on the input streamline set. The second stage refines the low-resolution vector field to a high- ...
Mar 5, 2018 · I would recommend this Distill article which talks in more details about using "deconvolution" layers vs "upscale + normal convolution".
In this paper, we propose a light weight convolutional neural network based SR model called LWSRNet for the upscaling factor x4.
Aug 2, 2023 · In this work, a deep-learning-based method is proposed to upscale the fine-scale geologic models, which can assist to improve upscaling efficiency ...