Awesome
Unsupervised Hyperspectral Pansharpening via Low-rank Diffusion Model (Information Fusion 2024)
<p align="center"> Xiangyu Rui, <a href="https://github.com/xiangyongcao">Xiangyong Cao</a>, <a href="https://github.com/LiPang">Li Pang</a>, <a href="https://github.com/Zeyu-Zhu">Zeyu Zhu</a>, <a href="https://github.com/zsyOAOA">Zongsheng Yue</a>, <a href="https://gr.xjtu.edu.cn/web/dymeng">Deyu Meng</a> </p> <p align="center">
Figure 1: The flowchart of the proposed method (PLRDiff). First, we estimate the coefficient matrix $E$ from the LRHS image. Second, the LRHS image, PAN image and the
coefficient matrix $E$ served as conditions are sent into a discretization of an SDE, where
we reconstruct the base tensor $\mathcal{A}$ by gradually sampling through a series of parameterized
distributions. Finally, the predicted $\mathcal{A}$ and $E$ are multiplied to form the desired HRHS
image.
1. Load pretrained Model
Pretrained diffusion model can be downloaded from
https://github.com/wgcban/ddpm-cd?tab=readme-ov-file#7-links-to-download-pre-trained-models
*Retraining or finetuning this model is not mandatory.
2. Dataset
Reproduce the results
All datasets used in this work can be found in Google Driver or BaiduCloud.
These datasets can be directly used to reproduce the results presented in the manuscript.
2.1. Origianl dataset links
Here are the links to the original datasets. You can download them, crop the data and generate your own datasets if interested.
Chikusei: https://naotoyokoya.com/Download.html
Houston: https://hyperspectral.ee.uh.edu/?page id=459
Pavia: https://github.com/liangjiandeng/HyperPanCollection
2.2. How do we generate the datasets
We generate our Chikusei and Houston datasets by 'data/generate_data.m'. Pavia can be directly downloaded from the above link for use.
3. Testing
3.1. Run the code
run python3 demo_syn.py -res opt
Before you running the script, please first download the pre-trained diffusion model, put it to your file and change the --resume in demo_syn.py.
3.2. Important Options
-dn: dataname,str. e.g. 'Chikusei'. The dataset should contain "HRMS", "LRMS" and "PAN".
-krtype: int. Set 0 for the first time in order to estimate kernel and srf. Set 1 if you have already save them in './estKR'.
-res: str. Set 'opt' for estimating the residual and 'no' for R=0.
Other options include eta1, eta2, scale, ks, step, accstep. Please refer to demo_syn.py.
4. Connections
<a href="mailto:xyrui.aca@gmail.com">xyrui.aca@gmail.com</a>