Tensor Compressive Sensing Fused Low-Rankness and Local-Smoothness
Authors: Xinling Liu, Jingyao Hou, Jiangjun Peng, Hailin Wang, Deyu Meng, Jianjun Wang
AAAI 2023 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | At last, we carry out experiments to apply our model to hyperspectral image and video restoration problems. The experimental results show that our method is prominently better than many other competing ones. |
| Researcher Affiliation | Academia | 1 School of Mathematics and Statistics, Southwest University, Chongqing 400715, China 2 School of Mathematics and Information, China West Normal University, Nanchong 637002, China 3 School of Mathematics and Statistics and Ministry of Education Key Lab of Intelligent Networks and Network Security, Xi an Jiaotong University, Xi an 710099, China 4 Macao Institute of Systems Engineering, Macau University of Science and Technology, Taipa, Macao |
| Pseudocode | Yes | Algorithm 1: ADMM for problem (19) |
| Open Source Code | Yes | Our code and Supplementary Material are available at https://github.com/fsliuxl/cs-tctv. |
| Open Datasets | Yes | We choose two typical datasets, HYDICE Washington DC Mall and HYDICE Urbanpart in this experiment. |
| Dataset Splits | Yes | Regularization parameters λ for our models are chosen empirically from the set {10 3, 10 2, 10 1, 1, 101, 102, 103} by cross-validation. |
| Hardware Specification | Yes | All experiments are run in MATLAB R2016a on a 64-bit PC with an E7-4820 2.00GHz CPU and 64GB memory. |
| Software Dependencies | Yes | All experiments are run in MATLAB R2016a |
| Experiment Setup | Yes | Regularization parameters λ for our models are chosen empirically from the set {10 3, 10 2, 10 1, 1, 101, 102, 103} by cross-validation. |