68 citations · 82 across the 11 of their papers we have counts for
11 papers
Self-Supervised One-Step Diffusion Refinement for Snapshot Compressive Imaging
Shaoguang Huang, Yunzhen Wang, Haijin Zeng +2
Snapshot compressive imaging (SCI) captures multispectral images (MSIs) using a single coded two-dimensional (2-D) measurement, but reconstructing high-fidelity MSIs from these com…
Unsupervised Spectral Demosaicing with Lightweight Spectral Attention Networks
Kai Feng, Yongqiang Zhao, Seong G. Kong +1
This paper presents a deep learning-based spectral demosaicing technique trained in an unsupervised manner. Many existing deep learning-based techniques relying on supervised learn…
Degradation-Noise-Aware Deep Unfolding Transformer for Hyperspectral Image Denoising
Haijin Zeng, Jiezhang Cao, Kai Feng +4
Hyperspectral imaging (HI) has emerged as a powerful tool in diverse fields such as medical diagnosis, industrial inspection, and agriculture, owing to its ability to detect subtle…
Inheriting Bayer's Legacy-Joint Remosaicing and Denoising for Quad Bayer Image Sensor
Haijin Zeng, Kai Feng, Jiezhang Cao +5
Pixel binning based Quad sensors have emerged as a promising solution to overcome the hardware limitations of compact cameras in low-light imaging. However, binning results in lowe…
MSFA-Frequency-Aware Transformer for Hyperspectral Images Demosaicing
Haijin Zeng, Kai Feng, Shaoguang Huang +5
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-me…
Low-rank Meets Sparseness: An Integrated Spatial-Spectral Total Variation Approach to Hyperspectral Denoising
Haijin Zeng, Shaoguang Huang, Yongyong Chen +2
Spatial-Spectral Total Variation (SSTV) can quantify local smoothness of image structures, so it is widely used in hyperspectral image (HSI) processing tasks. Essentially, SSTV ass…
Multi-mode Core Tensor Factorization based Low-Rankness and Its Applications to Tensor Completion
Haijin Zeng
Low-rank tensor completion has been widely used in computer vision and machine learning. This paper develops a novel multi-modal core tensor factorization (MCTF) method combined wi…
Enhanced nonconvex low-rank approximation of tensor multi-modes for tensor completion
Haijin Zeng, Xiaozhen Xie, Jifeng Ning
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 s…
Hyperspectral Image Denoising via Global Spatial-Spectral Total Variation Regularized Nonconvex Local Low-Rank Tensor Approximation
Haijin Zeng, Xiaozhen Xie, Jifeng Ning
Hyperspectral image (HSI) denoising aims to restore clean HSI from the noise-contaminated one. Noise contamination can often be caused during data acquisition and conversion. In th…
Hyperspectral Image Restoration via Global Total Variation Regularized Local nonconvex Low-Rank matrix Approximation
Haijin Zeng, Xiaozhen Xie, Jifeng Ning
Several bandwise total variation (TV) regularized low-rank (LR)-based models have been proposed to remove mixed noise in hyperspectral images (HSIs). Conventionally, the rank of LR…
Tensor completion using enhanced multiple modes low-rank prior and total variation
Haijin Zeng, Xiaozhen Xie, Jifeng Ning
In this paper, we propose a novel model to recover a low-rank tensor by simultaneously performing double nuclear norm regularized low-rank matrix factorizations to the all-mode mat…