Tensor FISTA-Net for Real-Time Snapshot Compressive Imaging
Authors: Xiaochen Han, Bo Wu, Zheng Shou, Xiao-Yang Liu, Yimeng Zhang, Linghe Kong10933-10940
AAAI 2020 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experimental results on synthetic datasets show that the proposed Tensor FISTA-Net achieves average PSNR improvement of 1.63 3.89d B over the state-of-the-art algorithms. |
| Researcher Affiliation | Academia | Xiaochen Han,1 Bo Wu,2 Zheng Shou,2 Xiao-Yang Liu,2 Yimeng Zhang,2 Linghe Kong1 1Shanghai Jiao Tong University, China 2Columbia University, USA |
| Pseudocode | No | The paper describes the iterative steps and modules of Tensor FISTA-Net through mathematical equations and descriptions, but does not present them in a structured pseudocode or algorithm block. |
| Open Source Code | Yes | The code is available at https://github.com/Guillermo Han97/SCI-Tensor-FISTA-Net. |
| Open Datasets | Yes | We evaluate the proposed Tensor FISTA-Net on three different synthetic datasets: Kobe (Yang et al. 2014), Park (Ma et al. 2019) and Vehicle (Ma et al. 2019). |
| Dataset Splits | No | Each testing dataset contains 32 frames of size 256 256 and B = 8, i.e., 4 measurements. We use the same training videos NBA, Central Park Aerial and Vehicle Crashing Tests in (Ma et al. 2019). |
| Hardware Specification | Yes | We do all the experiments on a server with an NVIDIA Telsa V100-PCIE GPU (16GB device memory). |
| Software Dependencies | No | We use Tensor Flow to implement our algorithm and do all the experiments on a server with an NVIDIA Telsa V100-PCIE GPU (16GB device memory). ... Adam optimizer (Kingma and Ba 2014) is used to minimize the training loss. |
| Experiment Setup | Yes | We set the number of phases as 10, learning rate as 0.0001 and running epoch as 500. Adam optimizer (Kingma and Ba 2014) is used to minimize the training loss. |