# Hyperspectral-Image-Denoising-Benchmark A list of hyperspectral image denoising resources collected by [Yongsen Zhao](https://github.com/junjun-jiang/Hyperspectral-Image-Denoising-Benchmark/blob/master/ https://github.com/seniusen) and [Junjun Jiang](https://github.com/junjun-jiang/Hyperspectral-Image-Denoising-Benchmark/blob/master/http://homepage.hit.edu.cn/jiangjunjun). #### Band-wise denoising methods - **[BM3D]** Image Denoising by Sparse 3-D Transform-Domain Collaborative Filtering, TIP2007, K. Dabov et al. - **[WNNM]** Weighted nuclear norm minimization with application to image denoising, CVPR2014, S. Gu et al. - **[EPLL]** From learning models of natural image patches to whole image restoration, ICCV2011, D. Zoran et al. #### Multi-band based methods ###### **[---Transform domain method---]** - Wavelet-based hyperspectral image estimation, IGARSS2003, I. Atkinson et al. - Hyperspectral image denoising using 3D wavelets, IGARSS2013, B. Rasti et al. - A nonlocal transform-domain filter for volumetric data denoising and reconstruction, TIP2012, M. Maggioni et al. - Hyperspectral Image Denoising Using First Order Spectral Roughness Penalty in Wavelet Domain, JStars2014, B. Rasti et al. ###### **[---Spatial domain methods---]** By adopting reasonable assumptions or priors, such as Global Correlation along Spetrum, Non-local Self Similarity (NSS) across space, Total Variation (TV), Non-local (Non-Local), Sparse Representation (SR), Low Rank (LR) models, Tensor models, etc., spatial domain based methods can well preserve the spatial and spectral characteristics. - **[GCS and NSS]** Adaptive Spatial-Spectral Dictionary Learning for Hyperspectral Image Denoising, ICCV2015, Ying Fu et al. - **[GCS and NSS and Tensor]** Decomposable nonlocal tensor dictionary learning for multispectral image denoising, CVPR2014, P. Yi et al. [[Code]](https://github.com/junjun-jiang/Hyperspectral-Image-Denoising-Benchmark/blob/master/http://gr.xjtu.edu.cn/c/document_library/get_file?folderId=1766524&name=DLFE-38410.zip) - **[GCS and NSS]** Multispectral images denoising by intrinsic tensor sparsity regularization, CVPR2016, Q. Xie et al. [[Code]](https://github.com/junjun-jiang/Hyperspectral-Image-Denoising-Benchmark/blob/master/http://gr.xjtu.edu.cn/c/document_library/get_file?folderId=2343711&name=DLFE-86323.zip) - **[GCS]** Denoising of hyperspectral images using the parafac model and statistical performance analysis, TGRS2012, X. F. Liu, et al. - **[TV]** Hyperspectral image denoising with cubic total variation model, ISPRS2012, H. Zhang et al. - **[TV]** Hyperspectral image denoising with a combined spatial and spectral hyperspectral total variation model, CJRS2014, G. Chen et al. - **[SR]** Spectral–Spatial Adaptive Sparse Representation for Hyperspectral Image Denoising, TGRS2016, T. Lu et al. - **[SR]** Noise removal from hyperspectral image with joint spectral-spatial distributed sparse representation, TGRS2016, J. Li et al. - **[LR]** Denoising and dimensionality reduction using multilinear tools for hyperspectral images, GRSL2008, N. Renard et al. - **[LR]** Hyperspectral image restoration using low-rank matrix recovery, TGRS2014, H. Zhang et al. [[PDF]](https://github.com/junjun-jiang/Hyperspectral-Image-Denoising-Benchmark/blob/master/http://www.lmars.whu.edu.cn/prof_web/zhanghongyan/papers/Hyperspectral%20Image%20Restoration%20Using%20Low-Rank%20Matrix%20Recovery.pdf)[[Code]](https://github.com/junjun-jiang/Hyperspectral-Image-Denoising-Benchmark/blob/master/http://www.lmars.whu.edu.cn/prof_web/zhanghongyan/resource/LRMR_HSI%20restoration.zip) - **[LR]** Hyperspectral Image Denoising via Noise-Adjusted Iterative Low-Rank Matrix Approximation, JStars2015, H. Zhang et al. [[PDF]](https://github.com/junjun-jiang/Hyperspectral-Image-Denoising-Benchmark/blob/master/http://www.lmars.whu.edu.cn/prof_web/zhanghongyan/papers/Hyperspectral%20Image%20Denoising%20via%20Noise-Adjusted%20Iterative%20Low-Rank%20Matrix%20Approximation.pdf)[[Code]](https://github.com/junjun-jiang/Hyperspectral-Image-Denoising-Benchmark/blob/master/http://www.lmars.whu.edu.cn/prof_web/zhanghongyan/resource/NAILRMA_HSI%20denoising.zip) - **[LR]** Hyperspectral image denoising via sparse representation and low-rank constraint, TGRS2015, Y. Zhao et al. [[Code]](https://github.com/junjun-jiang/Hyperspectral-Image-Denoising-Benchmark/blob/master/http://pan.baidu.com/s/1sjNTijj) - **[LR and GCS]** Hyperspectral Image Denoising Using Local Low-Rank Matrix Recovery and Global Spatial-Spectral Total Variation, JStars2015, H. Zhang et al. [[PDF]](https://github.com/junjun-jiang/Hyperspectral-Image-Denoising-Benchmark/blob/master/http://www.lmars.whu.edu.cn/prof_web/zhanghongyan/papers/Hyperspectral%20Image%20Denoising%20Using%20Local%20Low-Rank%20Matrix%20Recovery%20and%20Global%20Spatial-Spectral%20Total%20Variation.pdf)[[Code]](https://github.com/junjun-jiang/Hyperspectral-Image-Denoising-Benchmark/blob/master/http://www.lmars.whu.edu.cn/prof_web/zhanghongyan/resource/LLRGTV.rar) - **[[LR and Tensor]]** Hyper-Laplacian Regularized Unidirectional Low-rank Tensor Recovery for Multispectral Image Denoising, CVPR2017, Y. Chang et al. [[Code]](https://github.com/junjun-jiang/Hyperspectral-Image-Denoising-Benchmark/blob/master/http://www.escience.cn/system/file?fileId=91483) - **[LR]** Hyperspectral image restoration via iteratively regularized weighted Schatten p-norm minimization, TGRS2016, Y. Xie et al. - **[LR and Tensor]** Hyperspectral image restoration using low-rank tensor recovery, J-STARS2017, H. Fan et al. - **[LR]** Hyperspectral Image Restoration Using Low-Rank Representation on Spectral Difference Image, J-STARS2017, L. Sun et al. [[Code]](https://github.com/junjun-jiang/Hyperspectral-Image-Denoising-Benchmark/blob/master/http://www.escience.cn/system/file?fileId=90518) - **[LR]** Hyperspectral Image Denoising with Superpixel Segmentation and Low-Rank Representation, INS2017, F. Fan et al. [[Code]](https://github.com/junjun-jiang/Hyperspectral-Image-Denoising-Benchmark/blob/master/http://www.escience.cn/system/file?fileId=19) - **[LR]** Fast hyperspectral image denoising and inpainting based on low-rank and sparse representations, J-STARS2018, L. Zhuang et al. [[PDF]](https://github.com/junjun-jiang/Hyperspectral-Image-Denoising-Benchmark/blob/master/http://www.lx.it.pt/~bioucas/files/submitted_ieee_jstars_2017.pdf)[[Code]](https://github.com/junjun-jiang/Hyperspectral-Image-Denoising-Benchmark/blob/master/www.lx.it.pt/bioucas/code/Demo_FastHyDe_FastHyIn.rar) - **[LR and TV]** A Novel Anisotropic Total Variation Regularized Low Rank Method for Hyperspectral Image Mixed Denoising, ISPRS International Journal of Geo-Information, 2018, L. Sun et al. [[Code]](https://github.com/junjun-jiang/Hyperspectral-Image-Denoising-Benchmark/blob/master/http://www.escience.cn/system/file?fileId=102178) - **[LR]** Fast Superpixel based Subspace Low Rank Learning Method for Hyperspectral Denoising. IEEE Access,2018, L. Sun et al. [[Code]](https://github.com/junjun-jiang/Hyperspectral-Image-Denoising-Benchmark/blob/master/http://www.escience.cn/system/file?fileId=97575) - **[LR and TV and Tensor]** Spatial-Spectral Total Variation Regularized Low-Rank Tensor Decomposition forv Hyperspectral Image Denoising, TGRS2018, H. Fan et al. - **[LR]** Hyperspectral Image Denoising via Minimizing the Partial Sum of Singular Values and Superpixel Segmentation, Neurocomputing2018, Y. Liu et al. [[PDF]](https://github.com/junjun-jiang/Hyperspectral-Image-Denoising-Benchmark/blob/master/https://ac.els-cdn.com/S0925231218313705/1-s2.0-S0925231218313705-main.pdf?_tid=b9341448-ec31-43ee-9984-5b4653353341&acdnat=1543139827_163d7541ba7ab463ee3cc9dcfdcb09f4) - **[LR]** Non-local Meets Global: An Integrated Paradigm for Hyperspectral Denoising, arXiv2018, W. He et al. [[Web]](https://github.com/junjun-jiang/Hyperspectral-Image-Denoising-Benchmark/blob/master/https://sites.google.com/site/rshewei/home)[[Pdf]](https://github.com/junjun-jiang/Hyperspectral-Image-Denoising-Benchmark/blob/master/https://arxiv.org/pdf/1809.03298.pdf) - **[Tensor]** Color Image and Multispectral Image Denoising Using Block Diagonal Representation, arXiv2019, Zhaoming Kong et al. [[PDF]](https://github.com/junjun-jiang/Hyperspectral-Image-Denoising-Benchmark/blob/master/https://arxiv.org/pdf/1902.03954.pdf)[[Code]](https://github.com/junjun-jiang/Hyperspectral-Image-Denoising-Benchmark/blob/master/http://openremotesensing.net/wp-content/uploads/2018/03/MatlabCodes.zip) - Nonlocal Tensor-Ring Decomposition for Hyperspectral Image Denoising, IEEE TGRS 2019, Y. Chen et al. - Intracluster Structured Low-Rank Matrix Analysis Method for Hyperspectral Denoising, IEEE TGRS, Xiangtao Zheng et al.[[PDF]](https://github.com/junjun-jiang/Hyperspectral-Image-Denoising-Benchmark/blob/master/https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8527652) - Hyperspectral Image Denoising by Fusing the Selected Related Bands, IEEE TGRS, Xiangtao Zheng et al.[[PDF]](https://github.com/junjun-jiang/Hyperspectral-Image-Denoising-Benchmark/blob/master/https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8527652) - Nonlocal Low-Rank Regularized Tensor Decomposition for Hyperspectral Image Denoising, IEEE TGRS, Jize Xue et al.[[PDF]](https://github.com/junjun-jiang/Hyperspectral-Image-Denoising-Benchmark/blob/master/https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8657368) - A Novel Rank Approximation Method for Mixture Noise Removal of Hyperspectral Images, IEEE TGRS, Hailiang Ye et al.[[PDF]](https://github.com/junjun-jiang/Hyperspectral-Image-Denoising-Benchmark/blob/master/https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8632962) - A Low-Rank Tensor Dictionary Learning Method for Hyperspectral Image Denoising, IEEE TSP 2020, X. Gao et al. - Double Low-Rank Matrix Decomposition for Hyperspectral Image Denoising and Destriping, IEEE TGRS 2021, H. Zhang et al. - Hyperspectral Image Denoising Based on Global and Nonlocal Low-Rank Factorizations, IEEE TGRS 2021, L. Zhuang et al. - Deep spatio-spectral Bayesian posterior for hyperspectral image non-i.i.d. noise removal, ISPRS P&RS 2020, Q. Zhang et al. #### Deep learning methods - Hyperspectral imagery denoising by deep learning with trainable nonlinearity function, GRSL2017, W. Xie et al. - Hyperspectral Image Denoising Employing a Spatial-Spectral Deep Residual Convolutional Neural Network, TGRS2018, Q. Yuan et al. [[Code]](https://github.com/junjun-jiang/Hyperspectral-Image-Denoising-Benchmark/blob/master/https://github.com/WHUQZhang/HSID-CNN) - HSI-DeNet: Hyperspectral Image Restoration via Convolutional Neural Network, TGRS2019, Yi Chang et al. [[Web]](https://github.com/junjun-jiang/Hyperspectral-Image-Denoising-Benchmark/blob/master/http://www.escience.cn/people/changyi/index.html) [[Pdf]](https://github.com/junjun-jiang/Hyperspectral-Image-Denoising-Benchmark/blob/master/http://www.escience.cn/system/download/100951) - Deep Hyperspectral Prior: Denoising, Inpainting, Super-Resolution, arxiv2019, Oleksii Sidorov et al. [[Code]](https://github.com/junjun-jiang/Hyperspectral-Image-Denoising-Benchmark/blob/master/https://github.com/acecreamu/deep-hs-prior) [[Pdf]](https://github.com/junjun-jiang/Hyperspectral-Image-Denoising-Benchmark/blob/master/https://arxiv.org/pdf/1902.00301) - Hybrid Noise Removal in Hyperspectral Imagery With a Spatial-Spectral Gradient Network, IEEE TGRS 2019, Qiang Zhang et al. [[Code]](https://github.com/junjun-jiang/Hyperspectral-Image-Denoising-Benchmark/blob/master/https://github.com/WHUQZhang/SSGN) [[Pdf]](https://github.com/junjun-jiang/Hyperspectral-Image-Denoising-Benchmark/blob/master/https://arxiv.org/pdf/1810.00495) - Deep Spatial-spectral Representation Learning for Hyperspectral Image Denoising, IEEE TCI 2019, Weisheng Dong et al. [[Pdf]](https://github.com/junjun-jiang/Hyperspectral-Image-Denoising-Benchmark/blob/master/https://ieeexplore.ieee.org/document/8734833)[[Code]](https://github.com/junjun-jiang/Hyperspectral-Image-Denoising-Benchmark/blob/master/http://see.xidian.edu.cn/faculty/wsdong/Code_release/DENOISE_NG_ST.tar.gz) - Hyperspectral image denoising via matrix factorization and deep prior regularization, IEEE TIP 2019, B. Li. [[Pdf]](https://github.com/junjun-jiang/Hyperspectral-Image-Denoising-Benchmark/blob/master/https://ieeexplore.ieee.org/abstract/document/8767025) - A 3-D Atrous Convolution Neural Network for Hyperspectral Image Denoising, IEEE TGRS 2019， Wei Liu et al. - Hyperspectral Image Denoising Using SURE-Based Unsupervised Convolutional Neural Networks, IEEE TGRS 2020, Han V. Nguyen et al. - A Single Model CNN for Hyperspectral Image Denoising, IEEE TGRS 2020, Alessandro Maffei et al. - Hyperspectral image restoration via CNN denoiser prior regularized low-rank tensor recovery, CVIU 2020, H. Zeng et al. - Hyperspectral Image Denoising via Clustering-Based Latent Variable in Variational Bayesian Framework, IEEE TGRS 2021, Peyman Azimpour et al. - Uncertainty Quantification of Hyperspectral Image Denoising Frameworks Based on Sliding-Window Low-Rank Matrix Approximation, IEEE TGRS 2021, J. Song et al. - Deep Spatial-Spectral Global Reasoning Network for Hyperspectral Image Denoising, IEEE TGRS 2021, X. Cao et al. [[Code]](https://github.com/junjun-jiang/Hyperspectral-Image-Denoising-Benchmark/blob/master/https://github.com/xiangyongcao/GRN) - Hyperspectral Image Denoising Using a 3-D Attention Denoising Network, IEEE TGRS 2021, Q. Shi et al. - 3-D Quasi-Recurrent Neural Network for Hyperspectral Image Denoising, IEEE TNNLS 2021, K. Wei et al. #### Other methods for Non-i.i.d. Noise - Noise reduction of hyperspectral imagery using hybrid spatial-spectral derivative-domain wavelet shrinkage, TGRS2006, H. Othman et al. - Hyperspectral image denoising employing a spectral–spatial adaptive total variation model, TGRS2012, Q. Yuan et al. - 3-D nonlocal means filter with noise estimation for hyperspectral imagery denoising, IGRSS2013, Y. Qian et al. - Hyperspectral imagery restoration using nonlocal spectral-spatial structured sparse representation with noise estimation, JStars2013, Y. Qian et al. - Spectral–spatial kernel regularized for hyperspectral image denoising, IEE TGRS2015, Y. Yuan et al. - Denoising Hyperspectral Image with Non-i.i.d. Noise Structure, IEEE TCYB2017, Y. Chen et al. [[PDF]](https://github.com/junjun-jiang/Hyperspectral-Image-Denoising-Benchmark/blob/master/http://arxiv.org/pdf/1702.00098v1.pdf)[[Code]](https://github.com/junjun-jiang/Hyperspectral-Image-Denoising-Benchmark/blob/master/http://gr.xjtu.edu.cn/c/document_library/get_file?folderId=2406028&name=DLFE-88042.zip) - Hyperspectral Image Denoising by Fusing the Selected Related Bandsx, IEEE TGRS2018, X. Zheng et al. - A Novel Rank Approximation Method for Mixture Noise Removal of Hyperspectral Images, IEEE TGRS2018, X. Zheng et al. #### Databases - [CAVE dataset](https://github.com/junjun-jiang/Hyperspectral-Image-Denoising-Benchmark/blob/master/http://www.cs.columbia.edu/CAVE/databases/multispectral/) - [AVIRIS](https://github.com/junjun-jiang/Hyperspectral-Image-Denoising-Benchmark/blob/master/http://www.ehu.eus/ccwintco/index.php/Hyperspectral_Remote_Sensing_Scenes) - [ROSIS](https://github.com/junjun-jiang/Hyperspectral-Image-Denoising-Benchmark/blob/master/http://lesun.weebly.com/hyperspectral-data-set.html) - [HYDICE](https://github.com/junjun-jiang/Hyperspectral-Image-Denoising-Benchmark/blob/master/https://www.erdc.usace.army.mil/Media/Fact-Sheets/Fact-Sheet-Article-View/Article/610433/hypercube/) - [EO-1 Hyperion Data](https://github.com/junjun-jiang/Hyperspectral-Image-Denoising-Benchmark/blob/master/https://lta.cr.usgs.gov/ALI) - [Harvard dataset](https://github.com/junjun-jiang/Hyperspectral-Image-Denoising-Benchmark/blob/master/http://vision.seas.harvard.edu/hyperspec/explore.html) - [iCVL dataset](https://github.com/junjun-jiang/Hyperspectral-Image-Denoising-Benchmark/blob/master/http://icvl.cs.bgu.ac.il/hyperspectral/) - [NUS datase](https://github.com/junjun-jiang/Hyperspectral-Image-Denoising-Benchmark/blob/master/https://sites.google.com/site/hyperspectralcolorimaging/dataset/general-scenes) - [NTIRE18 dataset](https://github.com/junjun-jiang/Hyperspectral-Image-Denoising-Benchmark/blob/master/http://www.vision.ee.ethz.ch/ntire18/) #### Image Quality Measurement - Peak Signal to Noise Ratio (PSNR) - Structural SIMilarity index (SSIM) - Feature SIMilarity index (FSIM) - Erreur Relative Globale Adimensionnelle de Synthèse (ERGAS) - Spectral Angle Mapper (SAM)  Since 2022/5/7

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