Principal component analysis (PCA) and subspace estimation (SE) are popular data
analysis tools and used in a wide range of applications. The main interest in PCA/SE is for …

Background subtraction refers to extracting the foreground from an observed video, and is
the fundamental problem of various applications. There are two kinds of popular methods to …

In this paper, we propose a new non-convex regularization term named half-quadratic
function to achieve robustness and sparseness for robust principal component analysis, and …

This paper aims to recover a low-rank matrix and a sparse matrix from their superposition
observed in additive white Gaussian noise by formulating a convex optimization problem …

The traditional robust principal component analysis (RPCA) model is based on the nuclear
norm, which usually underestimates the singular values of the low‐rank matrix. As a …

Infrared small target detection in a complex background has always been a challenging task
in an infrared detection system. The existing methods based on the infrared patch image …

Traffic noise is often coupled with seismic signals when seismic data are acquired close to
the road. The traffic noise usually exhibits high amplitudes in the recorded seismic profile …

Moving object segmentation (MOS) using passive underwater image processing is an
important technology for monitoring marine habitats. It aids marine biologists studying …

Due to the fact that outliers can degrade localization accuracy significantly in wireless
sensor networks, we propose an outlier suppression approach via non-convex robust …

The improvement of the performance of online separating speech and music is an NP
problem and the separation optimization increases the complexity of the method in a Robust …