Deep Residual Networks have recently been shown to significantly improve the
performance of neural networks trained on ImageNet, with results beating all previous …

For the past two decades, there have been significant efforts to develop methods for object
detection in Remote Sensing (RS) images. In most cases, the datasets for small object …

Super resolution (SR) is an eminent system in the field of computer vison and image
processing to improve the visual perception of the poor-quality images. The key objective of …

Magnetic particle imaging (MPI) offers exceptional contrast for magnetic nanoparticles
(MNP) at high spatio-temporal resolution. A common procedure in MPI starts with a …

In this study, a deep learning-based approach is applied with the aim of reconstructing high-
resolution turbulent flow fields using minimal flow field data. A multi-scale enhanced super …

Turbulence is a complicated phenomenon because of its chaotic behavior with multiple
spatiotemporal scales. Turbulence also has irregularity and diffusivity, making predicting …

This paper deals with detecting small objects in remote sensing images from satellites or
any aerial vehicle by utilizing the concept of image super-resolution for image resolution …

This study presents a deep learning-based framework to recover high-resolution turbulent
velocity fields from extremely low-resolution data at various Reynolds numbers by utilizing …

Imaging methods have broad applications in geosciences. Scanning electron microscopy
(SEM) and micro-CT scanning have been applied for studying various geological problems …

With the advent of Deep Learning (DL), Super-Resolution (SR) has also become a thriving
research area. However, despite promising results, the field still faces challenges that …