The main aim of this paper is to develop a framelet representation of the tensor nuclear norm
for third-order tensor recovery. In the literature, the tensor nuclear norm can be computed by …

In this paper, we investigate tensor recovery problems within the tensor singular value
decomposition (t-SVD) framework. We propose the partial sum of the tubal nuclear norm …

The robust tensor recovery problem consists in reconstructing a tensor from a sample of
entries corrupted by noise, which has attracted great interest in a wide range of practical …

In this paper, we introduce a unified low-rank and sparse enhanced Tucker decomposition
model for tensor completion. Our model possesses a sparse regularization term to promote …

In this paper, we consider the robust tensor completion problem for recovering a low-rank
tensor from limited samples and sparsely corrupted observations, especially by impulse …

Robust low-rank tensor completion (RTC) problems have received considerable attention in
recent years such as in signal processing and computer vision. In this paper, we focus on …

Recently, the convex low-rank 3rd-order tensor recovery has attracted considerable
attention. However, there are some limitations to the convex relaxation approach, which may …

Infrared small target detection is a research hotspot in computer vision technology that plays
an important role in infrared early warning systems. Specifically, infrared images with strong …

Low-rank tensor recovery, as a higher order extension of low-rank matrix recovery, has
generated a great deal of research interests in recent years, such as image inpainting, video …

During the acquisition process, hyperspectral images (HSIs) are inevitably contaminated by
mixed noise, which seriously affects the image quality. To improve the image quality, HSI …