# PLRDiff

**Repository Path**: code_godtao/PLRDiff

## Basic Information

- **Project Name**: PLRDiff
- **Description**: No description available
- **Primary Language**: Unknown
- **License**: Not specified
- **Default Branch**: main
- **Homepage**: None
- **GVP Project**: No

## Statistics

- **Stars**: 0
- **Forks**: 0
- **Created**: 2024-04-12
- **Last Updated**: 2024-04-12

## Categories & Tags

**Categories**: Uncategorized

**Tags**: None

## README

# 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">

[[Draft arxiv](https://arxiv.org/pdf/2305.10925.pdf)] [[Main formal](https://www.sciencedirect.com/science/article/abs/pii/S1566253524001039)]

## Load pretrained Model 
Pretrained diffusion model can be downloaded from

[https://github.com/wgcban/ddpm-cd#arrow_forwardpre-trained-models--trainvaltest-logs](https://github.com/wgcban/ddpm-cd#arrow_forwardpre-trained-models--trainvaltest-logs)

## Download Dataset

Chikusei: [https://naotoyokoya.com/Download.html](https://naotoyokoya.com/Download.html)

Houston: [https://hyperspectral.ee.uh.edu/?page id=459](https://hyperspectral.ee.uh.edu/?page_id=459)

Pavia: [https://github.com/liangjiandeng/HyperPanCollection](https://github.com/liangjiandeng/HyperPanCollection)

## Prepare test dataset
Use data/generate_data.m to generate test data for Chikusei and Houston. Pavia can be directly downloaded for use. 

## Testing
### Single HSI testing
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.

there are several options you can set:

-gpu: int

-dn: dataname,str. e.g. 'Chikusei_test'. 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. 

## Connections
<a href="mailto:xyrui.aca@gmail.com">xyrui.aca@gmail.com</a>