HSIR
Out-of-box Hyperspectral Image Restoration Toolbox
Denoising for remotely sensed images from QRNN3D
Install
pip install hsir
Usage
Here are some runable examples, please refer to the code for more options.
python hsirun/train.py -a qrnn3d.qrnn3d
python hsirun/test.py -a qrnn3d.qrnn3d -r qrnn3d.pth -t icvl_512_50
Benchmark
Pretrained Models | Training Log | Datasets
Baidu Drive's Share Code=HSIR
Supported Models
| Denoising | Super Resolution | Spectral Compressive Imaging | Spectral Reconstruction |
Gaussian Denoising on ICVL
| Sigma=30 | Sigma=50 | Sigma=70 | Sigma=Blind | ||||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Params(M) | Runtime(s) | FLOPs | PSNR | SSIM | SAM | PSNR | SSIM | SAM | PSNR | SSIM | SAM | PSNR | SSIM | SAM | |
| Noisy | 18.59 | 0.110 | .0807 | 14.15 | 0.046 | 0.991 | 11.23 | 0.025 | 1.105 | 17.34 | 0.114 | 0.859 | |||
| BM4D | 154 | 38.45 | 0.934 | 0.126 | 35.60 | 0.889 | 0.169 | 33.70 | 0.845 | 0.207 | 37.66 | 0.914 | 0.143 | ||
| TDL | 18 | 40.58 | 0.957 | 0.062 | 38.01 | 0.932 | 0.085 | 36.36 | 0.909 | 0.105 | 39.91 | 0.946 | 0.072 | ||
| ITSReg | 907 | 41.48 | 0.961 | 0.088 | 38.88 | 0.941 | 0.098 | 36.71 | 0.923 | 0.112 | 40.62 | 0.953 | 0.087 | ||
| LLRT | 627 | 41.99 | 0.967 | 0.056 | 38.99 | 0.945 | 0.075 | 37.36 | 0.930 | 0.087 | 40.97 | 0.956 | 0.064 | ||
| KBR | 1755 | 41.48 | 0.984 | 0.088 | 39.16 | 0.974 | 0.100 | 36.71 | 0.961 | 0.113 | 40.68 | 0.979 | 0.080 | ||
| WLRTR | 1600 | 42.62 | 0.988 | 0.056 | 39.72 | 0.978 | 0.073 | 37.52 | 0.967 | 0.095 | 41.66 | 0.983 | 0.064 | ||
| NGmeet | 166 | 42.99 | 0.989 | 0.050 | 40.26 | 0.980 | 0.059 | 38.66 | 0.974 | 0.067 | 42.23 | 0.985 | 0.053 | ||
| HSID | 0.40 | 3 | 38.70 | 0.949 | 0.103 | 36.17 | 0.919 | 0.134 | 34.31 | 0.886 | 0.161 | 37.80 | 0.935 | 0.116 | |
| QRNN3D | 0.86 | 0.73 | 42.22 | 0.988 | 0.062 | 40.15 | 0.982 | 0.074 | 38.30 | 0.974 | 0.094 | 41.37 | 0.985 | 0.068 | |
| TS3C | 0.83 | 0.95 | 42.36 | 0.986 | 0.079 | 40.47 | 0.980 | 0.087 | 39.05 | 0.974 | 0.096 | 41.52 | 0.983 | 0.085 | |
| GRUNet | 14.2 | 0.87 | 42.84 | 0.989 | 0.052 | 40.75 | 0.983 | 0.062 | 39.02 | 0.977 | 0.080 | 42.03 | 0.987 | 0.057 |
Complex Denoising on ICVL
| non-iid | g+stripe | g+deadline | g+impulse | mixture | ||||||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Params(M) | Runtime(s) | FLOPs | PSNR | SSIM | SAM | PSNR | SSIM | SAM | PSNR | SSIM | SAM | PSNR | SSIM | SAM | PSNR | SSIM | SAM | |
| Noisy | 18.25 | 0.168 | 0.898 | 17.80 | 0.159 | 0.910 | 17.61 | 0.155 | 0.917 | 14.80 | 0.114 | 0.926 | 14.08 | 0.099 | 0.944 | |||
| LRMR | 32.80 | 0.719 | 0.185 | 32.62 | 0.717 | 0.187 | 31.83 | 0.709 | 0.227 | 29.70 | 0.623 | 0.311 | 28.68 | 0.608 | 0.353 | |||
| LRTV | 33.62 | 0.905 | 0.077 | 33.49 | 0.905 | 0.078 | 32.37 | 0.895 | 0.115 | 31.56 | 0.871 | 0.242 | 30.47 | 0.858 | 0.287 | |||
| NMoG | 34.51 | 0.812 | 0.187 | 33.87 | 0.799 | 0.265 | 32.87 | 0.797 | 0.276 | 28.60 | 0.652 | 0.486 | 27.31 | 0.632 | 0.513 | |||
| TDTV | 38.14 | 0.944 | 0.075 | 37.67 | 0.940 | 0.081 | 36.15 | 0.930 | 0.099 | 36.67 | 0.935 | 0.094 | 34.77 | 0.919 | 0.113 | |||
| HSID | 0.40 | 3 | 38.40 | 0.947 | 0.095 | 37.77 | 0.942 | 0.104 | 37.65 | 0.940 | 0.102 | 35.00 | 0.899 | 0.174 | 34.05 | 0.888 | 0.181 | |
| TS3C | 0.83 | 0.95 | 41.12 | 0.986 | 0.069 | 40.66 | 0.985 | 0.077 | 39.38 | 0.982 | 0.100 | 35.92 | 0.951 | 0.205 | 34.36 | 0.945 | 0.230 | |
| QRNN3D | 0.86 | 0.73 | 42.79 | 0.978 | 0.052 | 42.35 | 0.976 | 0.055 | 42.23 | 0.976 | 0.056 | 39.23 | 0.945 | 0.109 | 38.25 | 0.938 | 0.107 | |
| GRUNet | 14.2 | 0.87 | 42.89 | 0.992 | 0.047 | 42.39 | 0.991 | 0.050 | 42.11 | 0.991 | 0.050 | 40.70 | 0.985 | 0.067 | 38.51 | 0.981 | 0.081 |
Acknowledgement
Citation
If you find this repo helpful, please considering citing us.
@article{LAI2022281,
title = {Deep plug-and-play prior for hyperspectral image restoration},
journal = {Neurocomputing},
volume = {481},
pages = {281-293},
year = {2022},
issn = {0925-2312},
doi = {https://doi.org/10.1016/j.neucom.2022.01.057},
author = {Zeqiang Lai and Kaixuan Wei and Ying Fu},
}
Metadata
Release files for hsir 0.1.1
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
Source distribution (sdist)
| File | Size | Uploaded | |
|---|---|---|---|
| hsir-0.1.1.tar.gz | 94.0 kB | Details |
Built distribution (wheel)
| File | Interpreter | ABI | Platform | Reset |
|---|---|---|---|---|
| hsir-0.1.1-py3-none-any.whl | Python 3 | none | any | Details |
Total release size: 216.4 kB
Release files / hsir-0.1.1.tar.gz
| Download URL | hsir-0.1.1.tar.gz |
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| Size | 94.0 kB |
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