Background modeling for foreground detection is often used in different applications to
model the background and then detect the moving objects in the scene like in video …

Industrial process data are usually mixed with missing data and outliers which can greatly
affect the statistical explanation abilities for traditional data-driven modeling methods. In this …

Infrared small target detection techniques remain a challenging task due to the complex
background. To overcome this problem, by exploring context information, this research …

The detection of small infrared targets with low signal-to-noise ratios (SNRs) and contrasts in
noisy and cluttered backgrounds is challenging and therefore a domain of active research …

Noise causes unpleasant visual effects in low-light image/video enhancement. In this paper,
we aim to make the enhancement model and method aware of noise in the whole process …

The integration of data and scientific computation is driving a paradigm shift across the
engineering, natural, and physical sciences. Indeed, there exists an unprecedented …

As a convex relaxation of the rank minimization model, the nuclear norm minimization
(NNM) problem has been attracting significant research interest in recent years. The …

The simple and compact optics of lensless microscopes and the associated computational
algorithms allow for large fields of view and the refocusing of the captured images. However …

In pattern recognition and computer vision, one is often faced with scenarios where the
training data used to learn a model have different distribution from the data on which the …

The robust detection of small targets is one of the key techniques in infrared search and
tracking applications. A novel small target detection method in a single infrared image is …