Background/foreground separation is the first step in video surveillance system to detect
moving objects. Recent research on problem formulations based on decomposition into low …

Abstract Low Rank Regularization (LRR), in essence, involves introducing a low rank or
approximately low rank assumption to target we aim to learn, which has achieved great …

Recent advent of graph signal processing (GSP) has spurred intensive studies of signals
that live naturally on irregular data kernels described by graphs (eg, social networks …

In this paper, we explore the problem of multiview subspace clustering. We introduce a low-
rank tensor constraint to explore the complementary information from multiple views and …

Self-representation based subspace learning has shown its effectiveness in many
applications. In this paper, we promote the traditional subspace representation learning by …

Robust Principal Component Analysis (RPCA) via rank minimization is a powerful tool for
recovering underlying low-rank structure of clean data corrupted with sparse noise/outliers …

The heavy-tailed distributions of corrupted outliers and singular values of all channels in low-
level vision have proven effective priors for many applications such as background …

Recovering the image corrupted by additive white Gaussian noise (AWGN) and impulse
noise is a challenging problem due to its difficulties in an accurate modeling of the …

This study introduces a unified approach to tackle challenges in both low-level and high-
level vision tasks for image processing. The framework integrates faster nonconvex low-rank …

As a classic and effective edge detection operator, the Sobel operator has been widely used
in image segmentation and other image processing technologies. This operator has obvious …