OTPNet: ODE-inspired Tuning-free Proximal Network for Remote Sensing Image Fusion
Authors: Wei Yu, Zonglin Li, Qinglin Liu, Xin Sun
AAAI 2025 | Venue PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments on nine datasets across three different remote sensing image fusion tasks show that our OTPNet outperforms existing state-of-the-art approaches, which validates the effectiveness of our method. |
| Researcher Affiliation | Academia | School of Computer Science and Technology, Harbin Institute of Technology, China EMAIL, EMAIL |
| Pseudocode | No | The paper describes the algorithm using mathematical formulations and architectural diagrams (e.g., Figure 1, Figure 2) but does not include a formal pseudocode block or algorithm section with structured, numbered steps. |
| Open Source Code | No | The paper states: "For additional implementation details and metrics, please refer to the supplementary materials." but does not explicitly mention releasing code, nor does it provide a link to a code repository. |
| Open Datasets | Yes | To comprehensively evaluate our approach, we conduct experiments on nine fusion datasets corresponding to these three tasks: Pan-Sharpening task with World View3, Gao Fen-2, and Quick Bird satellite datasets; HSR task with Pavia Centre, Botswana4, and Chikusei datasets; MHF task with CAVE, Harvard and NTIRE2020 datasets. |
| Dataset Splits | Yes | For all datasets, we allocate 90% of the data to the training set and the remaining 10% to the validation set. |
| Hardware Specification | No | The paper does not specify any particular hardware (e.g., GPU model, CPU type) used for running the experiments. |
| Software Dependencies | No | The proposed OTPNet is implemented in Py Torch and trained using the Adam W optimizer with parameters set to β1 = 0.9 and β2 = 0.999. |
| Experiment Setup | Yes | The proposed OTPNet is implemented in Py Torch and trained using the Adam W optimizer with parameters set to β1 = 0.9 and β2 = 0.999. The training is conducted over 120k steps in a multistep schedule. The initial learning rate is set to 2e-4 and is reduced by half every 30k iterations. We adopt a batch size of 64 for all experiments. |