This is an example of how you may give instructions on setting up your project locally. To get a local copy up and running follow these simple example steps. Pretrained model will come soon.
This project is based on Python, PyTorch.
- requirements
ntorch>=0.4.1 torchvision>=0.2.1 dominate>=2.3.1 visdom>=0.1.8.3
- Add the NIR images to the folder: /dataset/Validation, please make sure the png images contains "nir" in the filename. Otherwise, please do necssary rectification in /data/VCIP_nir2rgb_dataset.py, class VCIPNir2RGBDataset_gen(BaseDataset): self.A_paths= [f for f in self.dir_A.glob('nir.png') if is_image(f)]
- In launch.json, please find the "name": "Python: Generate_results", change the "--gpu_ids" to "0" or "1" accordingly.
- Run the test-NIR.py
- Go to "/final_results/results/test_latest_iter950/images", the generated RGB files are inside this folder.
Alex - yangxingxing817@gmail.com
Huiyu Zhai - wenyu.zhy@gmail.com
Welcome to cite our paper if it inspires you!
@misc{zhai2024multiscale,
title={Multi-scale HSV Color Feature Embedding for High-fidelity NIR-to-RGB Spectrum Translation},
author={Huiyu Zhai and Mo Chen and Xingxing Yang and Gusheng Kang},
year={2024},
eprint={2404.16685},
archivePrefix={arXiv},
primaryClass={cs.CV}
}