187 citations · 1.2k across the 42 of their papers we have counts for
46 papers
Hierarchical rank-evolving representation for physics-informed neural networks
Ruoyang Su, Xi-Le Zhao, Kun Li +1
Recently, tensor-based physics-informed neural networks (T-PINNs) have received increasing attention. However, existing T-PINNs still face a fundamental challenge: they mainly rely…
Low-Rank Tensor Completion Based on Fractional Regularization with Ky Fan p-k Norm
Shan Fan, Feng Zhang, Jianjun Wang +2
This paper addresses low-rank tensor completion (LRTC) by proposing a novel nonconvex surrogate, namely the ratio of the tensor nuclear norm to the tensor Ky Fan p-k norm (TNPK), t…
Deciphering Neural Reparameterized Full-Waveform Inversion with Neural Sensitivity Kernel and Wave Tangent Kernel
Ruihua Chen, Yisi Luo, Bangyu Wu +2
Full-waveform inversion (FWI) estimates unknown parameters in the wave equation from limited boundary measurements. Recent advances in neural reparameterized FWI (NeurFWI) demonstr…
DTR: A Unified Deep Tensor Representation Framework for Multimedia Data Recovery
Ting-Wei Zhou, Xi-Le Zhao, Jian-Li Wang +4
Recently, the transform-based tensor representation has attracted increasing attention in multimedia data (e.g., images and videos) recovery problems, which consists of two indispe…
Hyperspectral and multispectral image fusion with arbitrary resolution through self-supervised representations
Ting Wang, Zipei Yan, Jizhou Li +3
The fusion of a low-resolution hyperspectral image (LR-HSI) with a high-resolution multispectral image (HR-MSI) has emerged as an effective technique for achieving HSI super-resolu…
NeurTV: Total Variation on the Neural Domain
Yisi Luo, Xile Zhao, Kai Ye +1
Recently, we have witnessed the success of total variation (TV) for many imaging applications. However, traditional TV is defined on the original pixel domain, which limits its pot…
Low-Tubal-Rank Tensor Recovery via Factorized Gradient Descent
Zhiyu Liu, Zhi Han, Yandong Tang +2
This paper considers the problem of recovering a tensor with an underlying low-tubal-rank structure from a small number of corrupted linear measurements. Traditional approaches tac…
Revisiting Nonlocal Self-Similarity from Continuous Representation
Yisi Luo, Xile Zhao, Deyu Meng
Nonlocal self-similarity (NSS) is an important prior that has been successfully applied in multi-dimensional data processing tasks, e.g., image and video recovery. However, existin…
An inexact proximal majorization-minimization Algorithm for remote sensing image stripe noise removal
Chengjing Wang, Xile Zhao, Qingsong Wang +2
The stripe noise existing in remote sensing images badly degrades the visual quality and restricts the precision of data analysis. Therefore, many destriping models have been propo…
SVDinsTN: A Tensor Network Paradigm for Efficient Structure Search from Regularized Modeling Perspective
Yu-Bang Zheng, Xi-Le Zhao, Junhua Zeng +4
Tensor network (TN) representation is a powerful technique for computer vision and machine learning. TN structure search (TN-SS) aims to search for a customized structure to achiev…
H2TF for Hyperspectral Image Denoising: Where Hierarchical Nonlinear Transform Meets Hierarchical Matrix Factorization
Jiayi Li, Jinyu Xie, Yisi Luo +2
Recently, tensor singular value decomposition (t-SVD) has emerged as a promising tool for hyperspectral image (HSI) processing. In the t-SVD, there are two key building blocks: (i)…
Uncertainty-Aware Unsupervised Image Deblurring with Deep Residual Prior
Xiaole Tang, Xile Zhao, Jun Liu +3
Non-blind deblurring methods achieve decent performance under the accurate blur kernel assumption. Since the kernel uncertainty (i.e. kernel error) is inevitable in practice, semi-…
Fast and Structured Block-Term Tensor Decomposition For Hyperspectral Unmixing
Meng Ding, Xiao Fu, Xi-Le Zhao
The block-term tensor decomposition model with multilinear rank- terms (or, the "LL1 tensor decomposition" in short) offers a valuable alternative for hyperspectral un…
Unsupervised Image Deraining: Optimization Model Driven Deep CNN
Changfeng Yu, Yi Chang, Yi Li +2
The deep convolutional neural network has achieved significant progress for single image rain streak removal. However, most of the data-driven learning methods are full-supervised…
Unsupervised Deraining: Where Contrastive Learning Meets Self-similarity
Yuntong Ye, Changfeng Yu, Yi Chang +4
Image deraining is a typical low-level image restoration task, which aims at decomposing the rainy image into two distinguishable layers: the clean image layer and the rain layer.…
Forecasting Sparse Movement Speed of Urban Road Networks with Nonstationary Temporal Matrix Factorization
Xinyu Chen, Chengyuan Zhang, Xi-Le Zhao +2
Movement speed data from urban road networks, computed from ridesharing vehicles or taxi trajectories, is often high-dimensional, sparse, and nonstationary (e.g., exhibiting season…
Nonlinear Transform Induced Tensor Nuclear Norm for Tensor Completion
Ben-Zheng Li, Xi-Le Zhao, Teng-Yu Ji +2
The linear transform-based tensor nuclear norm (TNN) methods have recently obtained promising results for tensor completion. The main idea of this type of methods is exploiting the…
Fully-Connected Tensor Network Decomposition for Robust Tensor Completion Problem
Yun-Yang Liu, Xi-Le Zhao, Guang-Jing Song +2
The robust tensor completion (RTC) problem, which aims to reconstruct a low-rank tensor from partially observed tensor contaminated by a sparse tensor, has received increasing atte…
Nonlocal Patch-Based Fully-Connected Tensor Network Decomposition for Remote Sensing Image Inpainting
Wen-Jie Zheng, Xi-Le Zhao, Yu-Bang Zheng +1
Remote sensing image (RSI) inpainting plays an important role in real applications. Recently, fully-connected tensor network (FCTN) decomposition has been shown the remarkable abil…
Self-Supervised Nonlinear Transform-Based Tensor Nuclear Norm for Multi-Dimensional Image Recovery
Yi-Si Luo, Xi-Le Zhao, Tai-Xiang Jiang +3
In this paper, we study multi-dimensional image recovery. Recently, transform-based tensor nuclear norm minimization methods are considered to capture low-rank tensor structures to…
Hyperspectral Denoising Using Unsupervised Disentangled Spatio-Spectral Deep Priors
Yu-Chun Miao, Xi-Le Zhao, Xiao Fu +2
Image denoising is often empowered by accurate prior information. In recent years, data-driven neural network priors have shown promising performance for RGB natural image denoisin…
Non-Local Robust Quaternion Matrix Completion for Color Images and Videos Inpainting
Zhigang Jia, Qiyu Jin, Michael K. Ng +1
The image nonlocal self-similarity (NSS) prior refers to the fact that a local patch often has many nonlocal similar patches to it across the image and has been widely applied in m…
Dictionary Learning with Low-rank Coding Coefficients for Tensor Completion
Tai-Xiang Jiang, Xi-Le Zhao, Hao Zhang +1
In this paper, we propose a novel tensor learning and coding model for third-order data completion. Our model is to learn a data-adaptive dictionary from the given observations, an…
Unsupervised Hyperspectral Mixed Noise Removal Via Spatial-Spectral Constrained Deep Image Prior
Yi-Si Luo, Xi-Le Zhao, Tai-Xiang Jiang +2
Recently, convolutional neural network (CNN)-based methods are proposed for hyperspectral images (HSIs) denoising. Among them, unsupervised methods such as the deep image prior (DI…