Developments in space-based hyperspectral sensors, advanced remote sensing, and
machine learning can help crop yield measurement, modelling, prediction, and crop …

Image denoising aims to restore a clean image from an observed noisy one. Model-based
image denoising approaches can achieve good generalization ability over different noise …

With the evolutionary development of modern communications technology, automatic
modulation classification (AMC) has played an increasing role in the complex wireless …

Image/video compression and communication need to serve both human vision and
machine vision. To address this need, we propose a scalable image compression solution …

Deep convolutional neural networks (CNNs) are significant in remote sensing. Due to the
strong local representation learning ability, CNNs have excellent performance in remote …

Global average pooling (GAP) plays an important role in traditional channel attention.
However, there is the disadvantage of insufficient information to use the result of GAP as the …

We propose a novel neural architecture for computer vision--WaveMix--that is resource-
efficient and yet generalizable and scalable. While using fewer trainable parameters, GPU …

The utilization of wavelet-based techniques in graph neural networks (GNNs) has gained
considerable attention, particularly in the context of node classification. Although existing …

Metal artifact reduction (MAR) is important for clinical diagnosis with CT images. The existing
state-of-the-art deep learning methods usually suppress metal artifacts in sinogram or image …

Abstract Background and Objective: Parkinson's disease (PD) is one of the most prevalent
neurodegenerative brain diseases worldwide. Therefore, accurate PD screening is crucial …