Awesome
Semantic Guided Large Scale Factor Remote Sensing Image Super-resolution with Generative Diffusion Prior
Officical code for Paper
<p align="center"> <img src="assets/architecture.png" style="border-radius: 15px"> </p>:book:Table Of Contents
<a name="visual_results"></a>:eyes:Visual Results
<!-- <details close> <summary>General Image Restoration</summary> -->Results on synthetic dataset
<img src="assets/visual_results/sync_qualitative.png"/>Results on real-world dataset
<img src="assets/visual_results/real_qualitative.png"/>Results for style guidance
<img src="assets/visual_results/style-guidance.png"/>Results for style sampling
<img src="assets/visual_results/style-sample.png"/><a name="installation"></a>:gear:Installation
# clone this repo
git clone https://github.com/wwangcece/SGDM.git
# create an environment with python >= 3.9
conda create -n SGDM python=3.9
conda activate SGDM
pip install -r requirements.txt
<a name="pretrained_models"></a>:dna:Pretrained Models
Download the model and place it in the checkpoints/ folder
<a name="dataset"></a>:bar_chart:Dataset
For copyright reasons, we can only provide the geographic sampling points in the data and the download scripts of the remote sensing images. To download vector maps, you need to register a maptiler account and subscribe to the package.
<a name="inference"></a>:crossed_swords:Inference
<a name="general_image_inference"></a> First please modify the validation data set configuration files at configs/dataset
Inference for synthetic dataset
python inference_refsr_batch_simu.py \
--ckpt checkpoints/SGDM-syn.ckpt \
--config configs/model/refsr_simu.yaml \
--val_config configs/dataset/reference_sr_val_simu.yaml \
--output path/to/your/outpath \
--steps 50 \
--device cuda:0 \
Inference for real-world dataset
For style sampling
python inference_refsr_batch_real.py \
--ckpt checkpoints/SGDM-real.ckpt \
--config configs/model/refsr_real.yaml \
--val_config configs/dataset/reference_sr_val_real.yaml \
--sample_style true \
--ckpt_flow_mean checkpoints/flow_tanh_mini_mean \
--ckpt_flow_std checkpoints/flow_tanh_mini_std \
--output path/to/your/outpath \
--steps 50 \
--device cuda:0 \
For style guidance
python inference_refsr_batch_real.py \
--ckpt checkpoints/SGDM-real.ckpt \
--config configs/model/refsr_real.yaml \
--val_config configs/dataset/reference_sr_val_real.yaml \
--output 50 path/to/your/outpath \
--steps 50 \
--device cuda:0 \
<a name="train"></a>:stars:Train
TBD
Citation
Please cite us if our work is useful for your research.
@article{wang2024semantic,
title={Semantic Guided Large Scale Factor Remote Sensing Image Super-resolution with Generative Diffusion Prior},
author={Wang, Ce and Sun, Wanjie},
journal={arXiv preprint arXiv:2405.07044},
year={2024}
}
Acknowledgement
This project is based on Diffbir. Thanks for their awesome work.
Contact
If you have any questions, please feel free to contact with me at cewang@whu.edu.cn.