Owing to the rapid development of sensor technology, hyperspectral (HS) remote sensing
(RS) imaging has provided a significant amount of spatial and spectral information for the …

Non-local low-rank tensor approximation has been developed as a state-of-the-art method
for hyperspectral image (HSI) restoration, which includes the tasks of denoising …

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 …

Hyperspectral images (HSIs) are often corrupted by a mixture of several types of noise
during the acquisition process, eg, Gaussian noise, impulse noise, dead lines, stripes, etc …

Recently, tensor sparsity modeling has achieved great success in the tensor completion
(TC) problem. In real applications, the sparsity of a tensor can be rationally measured by low …

As a promising way for analyzing data, sparse modeling has achieved great success
throughout science and engineering. It is well known that the sparsity/low-rank of a …

This paper presents a hypserspectral image (HSI) super-resolution method, which fuses a
low-resolution HSI (LR-HSI) with a high-resolution multispectral image (HR-MSI) to get high …

Hyperspectral images (HSIs) are normally corrupted by a mixture of various noise types,
which degrades the quality of the acquired image and limits the subsequent application. In …

Rain streaks removal is an important issue of the outdoor vision system and has been
recently investigated extensively. In this paper, we propose a novel tensor based video rain …

Background subtraction is a key prerequisite for a wide range of image processing
applications due to its pervasiveness in various contexts. In particular, video surveillance …