A plethora of previous studies indicates that making full use of multifarious intrinsic
properties of primordial data is a valid pathway to recover original images from their …

Social science approaches to missing values predict avoided, unrequested, or lost
information from dense data sets, typically surveys. The authors propose a matrix …

Fluorescence microscopy images captured in low light and short exposure time conditions
are always contaminated by photons and readout noises, which reduce the fluorescence …

This work presents a general framework regarding the recovery of matrices equipped with
hybrid low-rank and local-smooth properties from just a few measurements consisting of …

In this paper, we study the low-rank matrix optimization problem, where the loss function is
smooth but not necessarily convex, and the penalty term is a nonconvex (folded concave) …

Recovering low-rank matrices by nuclear norm minimization and local-smooth matrices by
total variation seminorm minimization are two common methods in the context of …

Based on the powerful restricted isometry property (RIP) and the coherence tools, this paper
develops two types of robust recovery results for a (non-convex) regularized ℓ 1− 2 …

The traditional robust principal component analysis (RPCA) model aims to decompose the
original matrix into low-rank and sparse components and uses the nuclear norm to describe …

The essential task of tensor data analysis focuses on the tensor decomposition and the
corresponding notion of rank. In this paper, by introducing the notion of tensor Singular …

The tail-minimization approach is applied to the joint sparse multiple measurement vector
(MMV) model and direction of arrival (DOA) estimations. The mechanism is to estimate the …