Infrared search and track (IRST) system is widely used in many fields, however, it's still a
challenging task to detect infrared small targets in complex background. This paper …

Deep unfolding networks (DUNs) have proven to be a viable approach to compressive
sensing (CS). In this work, we propose a DUN called low-rank CS network (LR-CSNet) for …

Due to the corruptions or noises that existed in real-world data sets, the affinity graphs
constructed by the classical spectral clustering-based subspace clustering algorithms may …

It has been proved that subspace clustering algorithms with weighted norm regularizers
usually outperform their special cases with non-weighted regularizers. However, it is difficult …

Low-rank representation (LRR) is a very competitive technique in many real-world
applications for its robustness on processing noisy or corrupted data. In this paper, a multi …

The low‐rank representation (LRR) method has recently gained enormous popularity due to
its robust approach in solving the subspace segmentation problem, particularly those …

Recovering low-rank subspaces for data sets becomes an attractive problem in recent years.
We proposed a new low-rank subspace learning algorithm, termed latent graph-regularized …

Motion cue is pivotal in moving object analysis, which is the root for motion segmentation
and detection. These preprocessing tasks are building blocks for several applications such …

Low-rank representation (LRR) and its extensions have shown prominent performances in
subspace segmentation tasks. Among these algorithms, structured-constrained low-rank …

Low-rank representation (LRR) is a very competitive technique in various real-world
applications for its powerful capability in discovering latent structure of noisy or corrupted …