Compared with radar and visible light imaging, infrared imaging has its own unique
advantages, and in recent years, it has become a topic of intense research interest. Robust …

Excellent performance, real time and strong robustness are three vital requirements for
infrared small target detection. Unfortunately, many current state-of-the-art methods merely …

To improve the detection ability of infrared small targets in complex backgrounds, a novel
method based on non-convex rank approximation minimization joint l 2, 1 norm (NRAM) was …

Hyperspectral images (HSIs) are often corrupted by a mixture of several types of noise
during the acquisition process, eg, Gaussian noise, impulse noise, dead lines, stripes, etc …

Many state-of-the-art methods have been proposed for infrared small target detection. They
work well on the images with homogeneous backgrounds and high-contrast targets …

Infrared remote sensing imaging has a wide range of military and civilian applications. The
detection of dim small targets is one of the most valuable research topics in this field …

It is believed that Gradient Descent (GD) induces an implicit bias towards good
generalization in training machine learning models. This paper provides a fine-grained …

Under the framework of graph-based learning, the key to robust subspace clustering and
subspace learning is to obtain a good similarity graph that eliminates the effects of errors …

Recently, deep-learning-based denoising methods for hyperspectral images (HSIs) have
been comprehensively studied and achieved impressive performance because they can …

Joint sparse representation (JSR) has shown great potential in various image processing
and computer vision tasks. Nevertheless, the conventional JSR is fragile to outliers. In this …