Enhancing Hyperspectral Images via Diffusion Model and Group-Autoencoder Super-resolution Network
Authors: Zhaoyang Wang, Dongyang Li, Mingyang Zhang, Hao Luo, Maoguo Gong
AAAI 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experimental results on both natural and remote sensing hyperspectral datasets demonstrate that the proposed method is superior to other state-of-the-art methods both visually and metrically. |
| Researcher Affiliation | Collaboration | Zhaoyang Wang1 2*, Dongyang Li2 3, Mingyang Zhang1 , Hao Luo2 3, Maoguo Gong1, 1Ministry of Education, Key Laboratory of Collaborative Intelligence Systems, Xidian University 2DAMO Academy, Alibaba Group, 310023, Hangzhou, China 3Hupan Lab, 310023, Hangzhou, China |
| Pseudocode | Yes | Algorithm 1: Testing process |
| Open Source Code | No | The paper does not include an unambiguous sentence stating that the authors are releasing their code or provide a direct link to a source-code repository for the described methodology. |
| Open Datasets | Yes | In our experiments, we used three publicly available datasets to validate the performance of our model. These datasets include two remote-sensing HSI datasets: Pavia Center (Pavia C) dataset and Chikusei dataset(Yokoya and Iwasaki 2016), and one natural image HSI dataset: Harvard dataset (Chakrabarti and Zickler 2011). |
| Dataset Splits | No | The paper mentions using specific datasets for training and evaluation but does not specify exact train/validation/test splits, sample counts for each split, or reference predefined splits with citations for reproducibility. |
| Hardware Specification | No | The paper does not explicitly describe the hardware used for running its experiments, such as specific GPU models, CPU models, or cloud computing instance types. |
| Software Dependencies | No | The paper mentions using the 'Adam optimizer' and a 'pre-trained SR3 diffusion model' but does not provide specific version numbers for these or any other key software components (e.g., Python, PyTorch, CUDA versions) needed for reproducibility. |
| Experiment Setup | Yes | We used the Adam optimizer with β1 = 0.9 and β2 = 0.999 for training, with a batch size of 8 for the Harvard dataset and 4 for the Pavia C and Chikusei datasets. The learning rate was set to 1e 4 during GAE training and reduced to 1e 5 for the diffusion model. During the training process, we utilized a pre-trained SR3 diffusion model. In the GAE module, bands were divided into subgroups of size 16 for Pavia C and Chikusei datasets, and 8 for the Harvard dataset, with one-quarter overlap between subgroups. |