activity
20172022
most citedWeighted Low-rank Tensor Recovery for Hyperspectral Image Restoration

31 citations · 84 across the 10 of their papers we have counts for

collaborators

20 papers

eess.SP2022

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…

cs.CV20222 cited

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…

cs.CV20213 cited

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…

cs.CV2021

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…

cs.CV202129 cited

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…

cs.CV2020

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…