All matlab codes of all experiments from the paper TSP2020-A Low-rank Tensor Dictionary Learning Method for Hyperspectral Image Denoising.
data: HSIDnet_data.mat : the test ICVL HSI of HSI-DeNet jasperRidge_10band.mat : watercolors_MSI.mat : lib: LTDL_utilize\ : functions of the LTDL method hyperspectralToolbox\ : HSI detection toolbox https://github.com/isaacgerg/matlabHyperspectralToolbox tensor_toolbox\ : tensor processing toolbox http://www.sandia.gov/~tgkolda/TensorToolbox/index‐2.5.html tensorlab\ : tensor processing toolbox https://www.tensorlab.net/versions.html#3.0 quality_assess\ : functions of quality assessment indices http://gr.xjtu.edu.cn/web/dymeng compete_methods: ksvdbox\ : http://www.cs.technion.ac.il/~ronrubin/software.html naonlm3d\ : http://personales.upv.es/jmanjon/denoising/arnlm.html BM3D\ : http://www.cs.tut.fi/~foi/GCF‐BM3D/ BM4D\ : http://www.cs.tut.fi/~foi/GCF‐BM3D/ tensor_dl\ : http://gr.xjtu.edu.cn/web/dymeng KBRreg\ : http://gr.xjtu.edu.cn/web/dymeng LLRT\ : http://www.escience.cn/people/changyi/codes.html HSI-DeNet1\ : http://www.escience.cn/people/changyi/codes.html MStSVD\ : https://github.com/ZhaomingKong/Hyperspectral_Image_denoising LRTA.m : http://gr.xjtu.edu.cn/web/dymeng PARAFAC.m : http://gr.xjtu.edu.cn/web/dymeng findFMeasure.m : function for getting GIF result myPlotROC.m : plotting ROC curves tight_subplot.m : creating "subplot" axes with adjustable gaps and margins Demo_DL_syn.m : Detect road on the denoised jasperRidge HSIs via different methods (Fig. 7, 8). Please run it where we provide the pre‐computing denoising results and you can get the results in Fig. 7 and Fig. 8. Demo_denoise_ge.m : The demo on "watercolors" HSI with generated noise. It needs to take a lot of time so you can test all methods on a cropped HSI. Change noise level by modifying variables "sigma_ratio" in your experiments. Demo_denoise_v2.m : Denoise on the test ICVL HSIs and the jasperRidge HSI. Set “exp=0” to compare model driven methods with deep learning method (Table IV) and set “exp=1” to denoise for target detection. To run the deep learning method in this demo, you should first download and install “MatConvNet”. Please see 'Readme.txt' in the path 'lib\compete_methods\HSI‐DeNet1'. Demo_target_detection.m : Test the proposed LTDL's dictionary learning performance with synthetic data (Fig. 4). You can see the pre‐computed results in the road of 'result\pre_synthetic_data_test_once'.
X. Gong, W. Chen and J. Chen, "A Low-Rank Tensor Dictionary Learning Method for Hyperspectral Image Denoising," in IEEE Transactions on Signal Processing, vol. 68, pp. 1168-1180, 2020, doi: 10.1109/TSP.2020.2971441.