Sparse representation and inversion have been widely used in the acquisition and
processing of geophysical data. In particular, the low-rank representation of seismic signals …

The robust and efficient detection of infrared small target is a key technique for infrared
search and track systems. Several robust principal component analysis (RPCA)-based …

Low-rank plus sparse matrix decomposition (LSD) is an important problem in computer
vision and machine learning. It has been solved using convex relaxations of the matrix rank …

Recently, weighted nuclear norm minimization (WNNM), which regularizes singular values
of an input matrix with different strengths according to given weights, has demonstrated …

A simple prior free factorization algorithm [??] is quite often cited work in the field of Non-
Rigid Structure from Motion (NRSfM). The benefit of this work lies in its simplicity of …

Incomplete observations in the data are always troublesome to data clustering algorithms. In
fact, most of the well-received techniques are not designed to encounter such imperative …

Noise of impulse type was common in medical images. In this paper, we modeled the
denoising problem for impulse noise by Weighted Schatten p-norm minimization (WSNM) …

Non-local self-similarity (NLSS) is widely used as prior information in an image restoration
method. In particular, a low-rankness-based prior has a significant effect on performance. On …

Global Navigation Satellite System (GNSS) coordinate time series contains obvious
seasonal signals, which mainly manifest as a superposition of annual and semi-annual …

The magnetic resonance (MR) images are acknowledged to be inevitably corrupted by
Rician distributed noise, which adversely affected the image quality for diagnosis purpose …