Welcome to Meng Ding's Homepage
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Meng Ding (丁猛) |
Research Interests
Image Processing
Tensor Analysis
Deep Learning
News
Aug. 2022: one paper has been accepted by ACM International Conference on Information and Knowledge Management (CIKM).
Jul. 2022: Check out this submission “Fast and Structured Block-Term Tensor Decomposition For Hyperspectral Unmixing”. Here we propose a new tensor decomposition-based algorithm for hyperspectral unmixing.
Dec. 2021: one paper “Tensor completion via nonconvex tensor ring rank minimization with guaranteed convergence” has been accepted by Signal Processing.
May 2021: one paper has been accepted by 29th European Signal Processing Conference (EUSIPCO), and two papers have been accepted by IEEE International Geoscience and Remote Sensing Symposium (IGARSS).
Dec. 2020: one paper “Hyperspectral Super-Resolution via Interpretable Block-Term Tensor Modeling” has been accepted by IEEE Journal of Selected Topics in Signal Processing.
Nov. 2020: Good news, our paper “Tensor train rank minimization with nonlocal self-similarity for tensor completion” has been accepted by Inverse Problems and Imaging.
June 2020: Check out this submission “Hyperspectral super-resolution via interpretable block-term tensor modeling”. Here we propose a new tensor-based hyperspectral super-resolution.
Feb. 2020: We submit “Tensor train rank minimization with nonlocal self-similarity for tensor completion”. In this work, we propose a new nonlocal tensor train rank-based tensor completion method by exploring the nonlocal self-similarity prior of tensor data.
Jan. 2020: J-H. Yang, X-L. Zhao, T-H. Ma, M. Ding, T-Z. Huang “Tensor train rank minimization with hybrid smoothness regularization for visual data recovery” has been accepted by Applied Mathematical Modelling.
Dec. 2019: M. Ding, T-Z. Huang, T-H. Ma, X-L. Zhao, J-H. Yang “Cauchy noise removal using group-based low-rank prior” has been accepted by Applied Mathematics and Computation.
Aug. 2019: M. Ding, T-Z. Huang, T-Y. Ji, X-L. Zhao, J-H. Yang “Low-Rank Tensor Completion Using Matrix Factorization Based on Tensor Train Rank and Total Variation” has been accepted by Journal of Scientific Computing.