2 citations · 4 across the 4 of their papers we have counts for
7 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…
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…
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…