hsir 0.1.1

Hyperspectral Image Restoration Toolbox

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
HSID-CNN (TGRS'2018) QRNN3D (TNNLS'2020) TS3C (NeurIPS'2021) GRUNet (Neurocomputing'2021)	Bi3DQRNN (JStar'2021) SSPSR (TCI'2020) MCNet (Remote Sense'2020) IFN (TGRS'2020) 3D-FCNN (Remote Sense'2017)	MST (CVPR'2022) CST (ECCV'2022) MST++ (CVPRW'2022) HDNET (CVPR'2022) BIRNAT (TPAMI'2022) DGSMP (CVPR'2021) GAP-Net (arxiv'2020) TSA-Net (ECCV'2020) ADMM-Net (ICCV'2019) λ-Net (ICCV'2019)

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

• QRNN3D
• DPHSIR
• MST
• TLC

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},
}