/Hyperspectral-Image-Denoising-Benchmark

A list of hyperspectral image denoising resources collected by Yongsen Zhao and Junjun Jiang.

Hyperspectral-Image-Denoising-Benchmark

A list of hyperspectral image denoising resources collected by Yongsen Zhao and Junjun Jiang.

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]
  • [GCS and NSS] Multispectral images denoising by intrinsic tensor sparsity regularization, CVPR2016, Q. Xie et al. [Code]
  • [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][Code]
  • [LR] Hyperspectral Image Denoising via Noise-Adjusted Iterative Low-Rank Matrix Approximation, JStars2015, H. Zhang et al. [PDF][Code]
  • [LR] Hyperspectral image denoising via sparse representation and low-rank constraint, TGRS2015, Y. Zhao et al. [Code]
  • [LR and GCS] Hyperspectral Image Denoising Using Local Low-Rank Matrix Recovery and Global Spatial-Spectral Total Variation, JStars2015, H. Zhang et al. [PDF][Code]
  • [[LR and Tensor]] Hyper-Laplacian Regularized Unidirectional Low-rank Tensor Recovery for Multispectral Image Denoising, CVPR2017, Y. Chang et al. [Code]
  • [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]
  • [LR] Hyperspectral Image Denoising with Superpixel Segmentation and Low-Rank Representation, INS2017, F. Fan et al. [Code]
  • [LR] Fast hyperspectral image denoising and inpainting based on low-rank and sparse representations, J-STARS2018, L. Zhuang et al. [PDF][Code]
  • [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]
  • [LR] Fast Superpixel based Subspace Low Rank Learning Method for Hyperspectral Denoising. IEEE Access,2018, L. Sun et al. [Code]
  • [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]
  • [LR] Non-local Meets Global: An Integrated Paradigm for Hyperspectral Denoising, arXiv2018, W. He et al. [Web][Pdf]
  • [Tensor] Color Image and Multispectral Image Denoising Using Block Diagonal Representation, arXiv2019, Zhaoming Kong et al. [PDF][Code]
  • 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]
  • Hyperspectral Image Denoising by Fusing the Selected Related Bands, IEEE TGRS, Xiangtao Zheng et al.[PDF]
  • Nonlocal Low-Rank Regularized Tensor Decomposition for Hyperspectral Image Denoising, IEEE TGRS, Jize Xue et al.[PDF]
  • A Novel Rank Approximation Method for Mixture Noise Removal of Hyperspectral Images, IEEE TGRS, Hailiang Ye et al.[PDF]
  • 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]
  • HSI-DeNet: Hyperspectral Image Restoration via Convolutional Neural Network, TGRS2019, Yi Chang et al. [Web] [Pdf]
  • Deep Hyperspectral Prior: Denoising, Inpainting, Super-Resolution, arxiv2019, Oleksii Sidorov et al. [Code] [Pdf]
  • Hybrid Noise Removal in Hyperspectral Imagery With a Spatial-Spectral Gradient Network, IEEE TGRS 2019, Qiang Zhang et al. [Code] [Pdf]
  • Deep Spatial-spectral Representation Learning for Hyperspectral Image Denoising, IEEE TCI 2019, Weisheng Dong et al. [Pdf][Code]
  • Hyperspectral image denoising via matrix factorization and deep prior regularization, IEEE TIP 2019, B. Li. [Pdf]
  • 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]
  • 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][Code]
  • 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

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)

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