User profiles for Haijin Zeng
 | Haijin ZengGhent University Verified email at ugent.be Cited by 285 |
Hyperspectral image restoration via CNN denoiser prior regularized low-rank tensor recovery
Hyperspectral images (HSIs) are widely used in various tasks such as mineral detection and
food safety. However, during the imaging process, they are often contaminated by various …
food safety. However, during the imaging process, they are often contaminated by various …
Hyperspectral Image Restoration via Global L1-2 Spatial–Spectral Total Variation Regularized Local Low-Rank Tensor Recovery
Hyperspectral images (HSIs) are usually corrupted by various noises, eg, Gaussian noise,
impulse noise, stripes, dead lines, and many others. In this article, motivated by the good …
impulse noise, stripes, dead lines, and many others. In this article, motivated by the good …
Multimodal core tensor factorization and its applications to low-rank tensor completion
Low-rank tensor completion has been widely used in computer vision and machine learning.
This paper develops a novel multimodal core tensor factorization (MCTF) method …
This paper develops a novel multimodal core tensor factorization (MCTF) method …
Enhanced nonconvex low-rank approximation of tensor multi-modes for tensor completion
Higher-order low-rank tensor arises in many data processing applications and has attracted
great interests. Inspired by low-rank approximation theory, researchers have proposed a …
great interests. Inspired by low-rank approximation theory, researchers have proposed a …
Hyperspectral image denoising via global spatial-spectral total variation regularized nonconvex local low-rank tensor approximation
Hyperspectral image (HSI) denoising aims to restore clean HSI from the noise-contaminated
one which is usually caused during data acquisition and conversion. In this paper, we …
one which is usually caused during data acquisition and conversion. In this paper, we …
All of low-rank and sparse: A recast total variation approach to hyperspectral denoising
Hyperspectral image (HSI) processing tasks frequently rely on spatial–spectral total variation
(SSTV) to quantify the local smoothness of image structures. However, conventional SSTV …
(SSTV) to quantify the local smoothness of image structures. However, conventional SSTV …
Tensor completion using bilayer multimode low-rank prior and total variation
In this article, we propose a novel bilayer low-rankness measure and two models based on
it to recover a low-rank (LR) tensor. The global low rankness of underlying tensor is first …
it to recover a low-rank (LR) tensor. The global low rankness of underlying tensor is first …
Msfa-frequency-aware transformer for hyperspectral images demosaicing
Hyperspectral imaging systems that use multispectral filter arrays (MSFA) capture only one
spectral component in each pixel. Hyperspectral demosaicing is used to recover the non-…
spectral component in each pixel. Hyperspectral demosaicing is used to recover the non-…
Unmixing Diffusion for Self-Supervised Hyperspectral Image Denoising
Hyperspectral images (HSIs) have extensive applications in various fields such as medicine
agriculture and industry. Nevertheless acquiring high signal-to-noise ratio HSI poses a …
agriculture and industry. Nevertheless acquiring high signal-to-noise ratio HSI poses a …
Hyperspectral image denoising via combined non-local self-similarity and local low-rank regularization
Hyperspectral images (HSIs) are usually corrupted by various noises during the image
acquisition process, eg, Gaussian noise, impulse noise, stripes, deadlines and many others. …
acquisition process, eg, Gaussian noise, impulse noise, stripes, deadlines and many others. …