hsir 0.0.1

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 script/train.py -a qrnn3d.qrnn3d
python script/test.py -a qrnn3d.qrnn3d -t icvl_512_30 icvl_512_50 --save_img

Benchmark

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	42.24	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	41.42	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

Citation

@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},
}