Complete and high-quality labeled dataset is indispensable for image classification.
Considering the often arduous task of data labeling, clustering algorithms are commonly …

In order to fast sample an image and accurately reconstruct the image from a small amount
of sampled data, we design a novel deep network for optimization-based algorithm mapping …

The detection of brain disease is an essential issue in medical and research areas. Deep
learning techniques have shown promising results in detecting and diagnosing brain …

Image compressed sensing technology, particularly algorithm unrolling networks, has
garnered significant attention in the field of compressed sensing due to their interpretability …

A high-accuracy flatness prediction model is the basis for realizing flatness control. Real
flatness is typically reflected as the strain distribution, which is a vector. However, it is difficult …

Infrared small target detection (IRSTD) has experienced fast developments in recent years
and been widely applied in civilian and military fields. The long imaging distance and …

Distributed compressive video sensing (DCVS) for wireless visual sensor networks faces
challenges due to computational limitations and bandwidth constraints. This paper presents …

Magnetic resonance imaging (MRI) in medical imaging plays a vital role in the clinical
diagnostic. The motivation behind reconstruction of MRI is to reduce the radiation exposure …