Feng Zhang

Orcid: 0000-0003-1000-8877

Affiliations:
  • Southwest University, School of Mathematics and Statistics, Chongqing, China


According to our database1, Feng Zhang authored at least 31 papers between 2018 and 2024.

Collaborative distances:
  • Dijkstra number2 of four.
  • Erdős number3 of four.

Timeline

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Bibliography

2024
Low-tubal-rank tensor completion via local and nonlocal knowledge.
Inf. Sci., February, 2024

2023
Low-Tubal-Rank tensor recovery with multilayer subspace prior learning.
Pattern Recognit., August, 2023

Randomized sampling techniques based low-tubal-rank plus sparse tensor recovery.
Knowl. Based Syst., 2023

Signal recovery adapted to a dictionary from non-convex compressed sensing.
Int. J. Comput. Sci. Math., 2023

High-Order Tensor Recovery Coupling Multilayer Subspace Priori with Application in Video Restoration.
Proceedings of the 31st ACM International Conference on Multimedia, 2023

2022
Generalized Nonconvex Approach for Low-Tubal-Rank Tensor Recovery.
IEEE Trans. Neural Networks Learn. Syst., 2022

Low-Rank High-Order Tensor Completion With Applications in Visual Data.
IEEE Trans. Image Process., 2022

A New Sufficient Condition for Non-Convex Sparse Recovery via Weighted $\ell _{r}\!-\!\ell _{1}$ Minimization.
IEEE Signal Process. Lett., 2022

Robust Low-Tubal-Rank Tensor Recovery From Binary Measurements.
IEEE Trans. Pattern Anal. Mach. Intell., 2022

2021
Low-Tubal-Rank Plus Sparse Tensor Recovery With Prior Subspace Information.
IEEE Trans. Pattern Anal. Mach. Intell., 2021

Robust low-rank tensor reconstruction using high-order t-SVD.
J. Electronic Imaging, 2021

Perturbation analysis of low-rank matrix stable recovery.
Int. J. Wavelets Multiresolution Inf. Process., 2021

An optimal condition of robust low-rank matrices recovery.
Int. J. Wirel. Mob. Comput., 2021

Tensor restricted isometry property analysis for a large class of random measurement ensembles.
Sci. China Inf. Sci., 2021

2020
Uniqueness Guarantee of Solutions of Tensor Tubal-Rank Minimization Problem.
IEEE Signal Process. Lett., 2020

RIP-based performance guarantee for low-tubal-rank tensor recovery.
J. Comput. Appl. Math., 2020

Robust principal component analysis with intra-block correlation.
Neurocomputing, 2020

The perturbation analysis of nonconvex low-rank matrix robust recovery.
CoRR, 2020

An Optimal Condition of Robust Low-rank Matrices Recovery.
CoRR, 2020

An analysis of noise folding for low-rank matrix recovery.
CoRR, 2020

Estimating Structural Missing Values Via Low-Tubal-Rank Tensor Completion.
Proceedings of the 2020 IEEE International Conference on Acoustics, 2020

Low-Tubal-Rank Tensor Recovery From One-Bit Measurements.
Proceedings of the 2020 IEEE International Conference on Acoustics, 2020

2019
A nonconvex penalty function with integral convolution approximation for compressed sensing.
Signal Process., 2019

Image denoising in impulsive noise via weighted Schatten p -norm regularization.
J. Electronic Imaging, 2019

Sharp sufficient condition of block signal recovery via <i>l</i> <sub>2</sub>/<i>l</i> <sub>1</sub>-minimisation.
IET Signal Process., 2019

Block-sparse signal recovery based on truncated ℓ 1 minimisation in non-Gaussian noise.
IET Commun., 2019

Coherence-Based Robust Analysis of Basis Pursuit De-Noising and Beyond.
IEEE Access, 2019

2018
Reconstruction analysis of block-sparse signal via truncated ℓ 2 / ℓ 1 -minimisation with redundant dictionaries.
IET Signal Process., 2018

Coherence-Based Performance Guarantee of Regularized 𝓁<sub>1</sub>-Norm Minimization and Beyond.
CoRR, 2018

Perturbations of Compressed Data Separation With Redundant Tight Frames.
IEEE Access, 2018

New Sufficient Conditions of Signal Recovery With Tight Frames via l<sub>1</sub>-Analysis Approach.
IEEE Access, 2018


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