This paper surveys machine-learning-based super-resolution reconstruction for vortical
flows. Super resolution aims to find the high-resolution flow fields from low-resolution data …

We apply supervised machine learning techniques to a number of regression problems in
fluid dynamics. Four machine learning architectures are examined in terms of their …

Field-of-view and resolution trade-offs in x-ray micro-computed-tomography (micro-CT)
imaging limit the characterization, analysis, and model development of multiscale porous …

In this paper, we are aiming for a general reference-based super-resolution setting: it does
not require the low-resolution image and the high-resolution reference image to be well …

Field-of-view and resolution trade-offs in X-Ray micro-computed tomography (micro-CT)
imaging limit the characterization, analysis and model development of multi-scale porous …

Seismic field data usually suffer from low-resolution (LR) image quality. In recent years, high-
resolution (HR) seismic data acquisitions are either done in high-density seismic surveys or …

抄録 Machine-learned super-resolution is performed to reconstruct the high-resolution flow
(HR) field from low-resolution (LR) fluid flow data. As preliminary tests, we use two …

The success of deep learning models in fluid dynamics applications will depend on their
ability to handle sparse and noisy data accurately. This paper concerns the development of …

This paper proposes a deep-learning-based image enhancement approach that can
generate high-resolution micro-CT-like images from multidetector computed tomography …

In the pulp and paper industry, pulp testing is typically a labor-intensive process performed
on hand-made laboratory sheets. Online quality control by automated image analysis and …