Sp3ctralMamba: Physics-Driven Joint State Space Model for Hyperspectral Image Reconstruction
Authors: Ge Meng, Jingyan Tu, Jingjia Huang, Yunlong Lin, Yingying Wang, Xiaotong Tu, Yue Huang, Xinghao Ding
AAAI 2025 | Venue PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments on both simulated and real datasets demonstrate that Sp3ctral Mamba significantly elevates HSI reconstruction performance to a new level, surpassing SOTA methods in both quantitative and qualitative metrics. Experiments on different datasets demonstrate the effectiveness of Sp3ctral Mamba. Ablation experiments demonstrate the effectiveness of these constraints. |
| Researcher Affiliation | Academia | 1 Key Laboratory of Multimedia Trusted Perception and Efficient Computing, Ministry of Education of China, Xiamen University, China 2School of Informatics, Xiamen University, China 3Institute of Artificial Intelligence, Xiamen University, China EMAIL, EMAIL |
| Pseudocode | No | The paper describes its methodology in natural language and mathematical equations, accompanied by architectural diagrams (Figures 2 and 3), but does not include any explicitly labeled pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not contain any explicit statements about releasing source code, nor does it provide a link to a code repository. |
| Open Datasets | Yes | For simulated data, we used two widely-used hyperspectral datasets: CAVE (Park et al. 2007) and KAIST (Choi et al. 2017). |
| Dataset Splits | Yes | Consistent with the settings in TSA-Net (Meng, Ma, and Yuan 2020), we used the CAVE dataset as the training set and selected 10 scenes from KAIST as the testing set. The patch size during training is 256 256. |
| Hardware Specification | Yes | We implemented Sp3ctral Mamba on a PC with a single NVIDIA RTX 4090 GPU |
| Software Dependencies | No | While the paper mentions building the network in the PyTorch framework ('we built our network in the Py Torch framework'), it does not specify a version number for PyTorch or any other software components. |
| Experiment Setup | Yes | The learning rate was set to 4 10 4 and the batch size was set to 4. In the initial 200 epochs, we used the reconstruction loss Lrec to optimize the predicted results. In the next 50 epochs, we stopped updating the decoder s gradients and introduced the energy prior LE to enhance the encoder s representation of overall pixel intensity. In the final 50 epochs, we did the opposite and introduced the structure prior LS to enhance the decoder s representation of edge details. |