Compressive spectral imaging (CSI) has attracted significant attention since it employs
synthetic apertures to codify spatial and spectral information, sensing only 2D projections of …

The fusion of a low-spatial-and-high-spectral resolution hyperspectral image (HSI) with a
high-spatial-and-low-spectral resolution multispectral image (MSI) allows synthesizing a …

Image fusion aims at estimating a high-resolution spectral image from a low-spatial-
resolution hyperspectral image and a low-spectral-resolution multispectral image. In this …

Compressive Sensing (CS) has emerged as a transformative technique in image
compression, offering innovative solutions to challenges in efficient signal representation …

Image fusion is a technique of combining two or more images into a single image which is
more informative from an interpretation point of view. With the rapid development of different …

Spectral image classification uses the huge amount of information provided by spectral
images to identify objects in the scene of interest. In this sense, spectral images typically …

In this paper, we evaluate the performance and analyze the explainability of machine
learning models boosted by feature selection in predicting COVID-19-positive cases from …

The optical codification in compressive spectral imaging has been an area of continuous
improvement. The study of the coded apertures as modulation devices and their contribution …

针对在实际应用场景下, 小麦不完善粒识别数据较少所产生识别率不佳的问题,
提出并实现了基于迁移学习的CNN-DBN 小麦不完善粒识别方法. 利用基于大型公开数据集 …

Spectral imaging offers useful additional information to improve or expand imaging
applications such as biomedical images, identification of cultures, and surveillance. These …