An HSI denoiser: Global Local Factorization (GLF)
The code and data herein distributed reproduce the results published in the paper
Lina Zhuang and Jose M. Bioucas-Dias, "Hyperspectral image denoising based on global and non-local low-rank factorizations", IEEE International Conference on Image Processing, Sep. 2017.
URL: https://www.it.pt/Publications/DownloadPaperConference/30727
% Description:
main.m ---- main script reproducing the denoising results published in GLF paper MPSNR.m ---- Performance criteria: mean peak signal-to-noise per band MSSIM.m & ssim_index ---- Performance criteria: mean structural similarity per band img_clean_dc.mat & img_clean_pavia.mat ---- Simulated clean datasets GLF_denoiser.m ---- denoising algorithm GLF NonlocalPatch_local_LR.m ---- Nonlocal patch-based denoising of subsapce coefficients LowRank_tensor.m ---- Low-rank approximation based filtering for similar groups of patches LowRankRecovery3.m ---- Low rank matrix recovery hysime.m ---- Subsapce identification
% Notes:
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Package instalation: unzip the files to a directory and run the scripts of "main.m", which reproduces the denoising results reported in the above paper.
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The script GLF_denoiser.m use the functions (e.g., tensor()) from Tensor Toolbox (version 2.6 (February 6, 2015)).
The MATLAB Tensor Toolbox Version 2.6 is available at the web page: http://www.sandia.gov/~tgkolda/TensorToolbox/index-2.6.html
Download this toolbox and install it in the folder /tensor_toolbox
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GLF_denoiser.m is the core funtion. It is a state-of-the-art denoiser designed for hyperspectral images corrupted with additive Gaussian noise.
% ACKNOWLEDGMENTS
The authors acknowledge the following individuals and organisations:
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Prof. Paolo Gamba from Pavia university, for making the Pavia University data set available to the community.
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Prof. David Landgrebe and Larry Biehl from Purdue University, for making the Washington DC Mall data set available to the community.
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Authors of the MATLAB Tensor Toolbox (Brett W. Bader, Tamara G. Kolda, Jimeng Sun, Evrim Acar, Daniel M. Dunlavy, Eric C. Chi, Jackson Mayo, et al.) from Sandia National Laboratories, for making the Tensor Toolbox available to the community.
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Author: Lina Zhuang and Jose M. Bioucas Dias, Nov. 2017