As a convex relaxation of the rank minimization model, the nuclear norm minimization
(NNM) problem has been attracting significant research interest in recent years. The …

To improve the detection ability of infrared small targets in complex backgrounds, a novel
method based on non-convex rank approximation minimization joint l 2, 1 norm (NRAM) was …

The recent advances in hardware and imaging systems made the digital cameras
ubiquitous. Although the development of hardware has steadily improved the quality of …

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 …

Low rank matrix approximation (LRMA), which aims to recover the underlying low rank
matrix from its degraded observation, has a wide range of applications in computer vision …

Low-rank matrix approximation has been successfully applied to numerous vision problems
in recent years. In this paper, we propose a novel low-rank prior for blind image deblurring …

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 …

To further enhance the small targets and suppress the heavy clutters simultaneously, a
robust non-negative infrared patch-image model via partial sum minimization of singular …

This paper introduces a new high dynamic range (HDR) imaging algorithm which utilizes
rank minimization. Assuming a camera responses linearly to scene radiance, the input low …

Infrared (IR) small-target detection has been a widely studied task in IR search and tracking
systems. It remains a challenging problem, especially in heterogeneous scenarios, where it …