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Non-local Meets Global: An Integrated Paradigm for Hyperspectral Denoising [article]

Wei He, Quanming Yao, Chao Li, Naoto Yokoya, Qibin Zhao
2019 arXiv   pre-print
Non-local low-rank tensor approximation has been developed as a state-of-the-art method for hyperspectral image (HSI) denoising.  ...  Then, the non-local low-rank denoising and iterative regularization are developed to refine the reduced image and projection, respectively.  ...  After the non-local low-rank modeling was first introduced to HSI denoising in [31] , the flowchart of the non-local based methods become fixed: FBPs grouping and low-rank tensor approximation.  ... 
arXiv:1812.04243v2 fatcat:zhgt2csk2bafbontevvempaidu

Tensor-Based Low-Rank and Sparse Prior Information Constraints for Hyperspectral Image Denoising

Guxi Wang, Hongwei Han, Emmanuel John M. Carranza, Si Guo, Ke Guo, Keyan Xiao
2020 IEEE Access  
To remove noise, this paper, based on low-rank tensor decomposition, combined with non-local self-similar prior information, proposes a tensor-based non-local low-rank denoising model, where non-local  ...  INDEX TERMS Hyperspectral image denoising, non-local self-similar, low-rank tensor decomposition, sparse representation. planning [5] , and so on.  ...  For detailed discussion, please refer to [35] . III. TENSOR-BASED NON-LOCAL LOW-RANK DENOISING MODEL A.  ... 
doi:10.1109/access.2020.2996303 fatcat:nxwvzhehxncklo2vq6x26coa7a

Hyperspectral Image Denoising Using Group Low-Rank and Spatial-Spectral Total Variation

Taner Ince
2019 IEEE Access  
First, group low-rank exploits the local similarity inside patches and non-local similarity between patches which brings extra structural information.  ...  INDEX TERMS Denoising, hyperspectral image (HSI), mixed noise, group low-rank, spatial-spectral total variation (SSTV).  ...  The low-rank spectral non-local approach is used in [11] which includes the low-rank representation of precleaned image patches and the application of spectral non-local method to restore the image.  ... 
doi:10.1109/access.2019.2911864 fatcat:fzh5fewd65d4rfs5nnts3zvyc4

Hyperspectral Image Denoising Based on Nonlocal Low Rank Dictionary Learning

Zeng ZhiHua, Zhou Bing, Li Cong
2015 Open Automation and Control Systems Journal  
In allusion to hyperspectral remote sensing image denoising problem, the article proposes an image denoising algorithm based on nonlocal low rand dictionary learning.  ...  Firstly, combine the strong correlation of waveband images, the nonlocal self-similarity and the local sparseness to establish nonlocal low rank dictionary learning model.  ...  of the image itself, the article proposes an image denoising algorithm based on non-local low rank dictionary learning: firstly, establish nonlocal low rank dictionary learning model; then, construct corresponding  ... 
doi:10.2174/1874444301507011813 fatcat:f73sun46zbaztcfzjtbzxo5e44

Fast Hyperspectral Image Denoising and Inpainting Based on Low-Rank and Sparse Representations

Lina Zhuang, Jose M. Bioucas-Dias
2018 IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing  
FastHyDe and FastHyIn fully exploit extremely compact and sparse HSI representations linked with their low-rank and self-similarity characteristics.  ...  This paper introduces two very fast and competitive hyperspectral image (HSI) restoration algorithms: fast hyperspectral denoising (FastHyDe), a denoising algorithm able to cope with Gaussian and Poissonian  ...  and thus suitable to be denoised with non-local patch-based methods.  ... 
doi:10.1109/jstars.2018.2796570 fatcat:qrynb2dadzhf5jipd653zxoiau

Hyperspectral Image Denoising via Combined Non-Local Self-Similarity and Local Low-Rank Regularization

Haijin Zeng, Xiaozhen Xie, Wenfeng Kong, Shuang Cui, Jifeng Ning
2020 IEEE Access  
In this paper, a spatial non-local and local rank-constrained low-rank regularized Plugand-Play (NLRPnP) model is presented for mixed noise removal in HSIs.  ...  INDEX TERMS Hyperspectral images, denoising, plug-and-play framework, local low-rank matrix recovery, non-local regularization. 50190 This work is licensed under a Creative Commons Attribution 4.0 License  ...  Then, we adopt the patch-based low-rank matrix approximation to guarantee the local low-rankness while plugging in non-local based denoisers to promote the non-local self-similarity.  ... 
doi:10.1109/access.2020.2979809 fatcat:fpru4mgxjzarrdso7jwn5yskza

Non-local Meets Global: An Iterative Paradigm for Hyperspectral Image Restoration [article]

Wei He and Quanming Yao and Chao Li and Naoto Yokoya and Qibin Zhao and Hongyan Zhang and Liangpei Zhang
2020 arXiv   pre-print
Non-local low-rank tensor approximation has been developed as a state-of-the-art method for hyperspectral image (HSI) restoration, which includes the tasks of denoising, compressed HSI reconstruction and  ...  Subsequently, non-local low-rank denoising is developed to refine the reduced image and orthogonal basis iteratively.  ...  the non-local low-rank based methods. stage A: spectral low-rank denoising; stage B: spatial non-local low-rank denoising.  ... 
arXiv:2010.12921v1 fatcat:5itaicjr6vhf5f7do4xd5j7hpe

Group sparse nonnegative matrix factorization for hyperspectral image denoising

Yangyang Xu, Yuntao Qian
2016 2016 IEEE International Geoscience and Remote Sensing Symposium (IGARSS)  
Recently, HSI denoising methods using low rank representation and sparse coding have attracted much attention.  ...  Spectral signatures across different nonlocal similar FBPs partially share a set of bases, which means that each of them may remain some non-shared bases.  ...  Since the original nonlocal means algorithm was proposed for image denoising [3] , non-locality has become a widely used strategy for denoising.  ... 
doi:10.1109/igarss.2016.7730815 dblp:conf/igarss/XuQ16 fatcat:57kwwrfpoffnfajcmlff2h67ku

Hyperspectral Image Mixed Noise Removal Using Subspace Representation and Deep CNN Image Prior

Lina Zhuang, Michael K. Ng, Xiyou Fu
2021 Remote Sensing  
data, namely low-rankness in the spectral domain and high correlation in the spatial domain.  ...  The decreased SNR reduces the reliability of measured features or information extracted from HSIs, thus calling for effective denoising techniques.  ...  We refer to representative work on global and nonlocal low-rank factorizations (GLF) [17] , fast hyperspectral denoising (FastHyDe) [8] , and non-local meets global (NGmeet) [18] .  ... 
doi:10.3390/rs13204098 fatcat:42vmcbvvqrfipnvuxdscuuuzly

Variational Low-Rank Matrix Factorization with Multi-Patch Collaborative Learning for Hyperspectral Imagery Mixed Denoising

Shuai Liu, Jie Feng, Zhiqiang Tian
2021 Remote Sensing  
In this study, multi-patch collaborative learning is introduced into variational low-rank matrix factorization to suppress mixed noise in hyperspectral images (HSIs).  ...  framework for each collaborative patch.  ...  The global and non-local low-rank factorization (GLF) was proposed to suppress the noises in HSIs by utilizing the low dimensional sub-spaces and the self-similarity of the real HSI [26] .  ... 
doi:10.3390/rs13061101 fatcat:zspgtwkujnev3phhtlu75vtgpq

Proximal approach to denoising hyperspectral images under mixed-noise model

Hazique Aetesam, Kumari Poonam, Suman Kumar Maji
2020 IET Image Processing  
The authors present a proximal approach to hyperspectral image denoising adapted to the mixed noise behaviour of hyperspectral data; named hyperspectral image proximal denoiser (HSIProxDenoiser).  ...  Hence, including both regularisation terms can help achieve the desired denoising performance.  ...  A denoising framework guided by the group low-rank property to detect local similarity within the patch and non-local similarity among patches is utilised in [25] .  ... 
doi:10.1049/iet-ipr.2019.1763 fatcat:fm3wttchqrgj3fulxkmho7xobe

Constrained low-tubal-rank tensor recovery for hyperspectral images mixed noise removal by bilateral random projections [article]

Hao Zhang, Xi-Le Zhao, Tai-Xiang Jiang, Michael Kwok-Po Ng
2019 arXiv   pre-print
In this paper, we propose a novel low-tubal-rank tensor recovery model, which directly constrains the tubal rank prior for effectively removing the mixed Gaussian and sparse noise in hyperspectral images  ...  Experimental examples for hyperspectral image denoising are presented to demonstrate the effectiveness and efficiency of the proposed method.  ...  The objective value X − L − S 2 F converges to a local minimum based on the framework in [13] . Algorithm 1: CLTRTR for HSIs denoising.  ... 
arXiv:1905.05941v1 fatcat:oxq2p5237jfprac4ygbqzaldna

Non-local similarity based tensor decomposition for hyperspectral image denoising

Fan Xu, Xiao Bai, Jun Zhou
2017 2017 IEEE International Conference on Image Processing (ICIP)  
An iterative denoising strategy is adopted for better effect in practice.  ...  Then the task of hyperspectral image denoising is transformed into a high order tensor approximation problem, which can be efficiently solved by alternating optimization.  ...  Accordingly, the low-rank approximation (LRTA) [15] utilizes the Tucker decomposition [16] of the input HSI. Liu et al. designed a PARAFAC [17, 18] method by employing parallel factor analysis.  ... 
doi:10.1109/icip.2017.8296610 dblp:conf/icip/XuBZ17 fatcat:zfz4fyki7zaq3eaxkpjo5bdeji

FastHyMix: Fast and Parameter-free Hyperspectral Image Mixed Noise Removal [article]

Lina Zhuang, Michael K. Ng
2021 arXiv   pre-print
model and exploits two main characteristics of hyperspectral data, namely low-rankness in the spectral domain and high correlation in the spatial domain.  ...  The proposed method takes advantage of the low-rankness using subspace representation and the spatial correlation of HSIs by adding a powerful deep image prior, which is extracted from a neural denoising  ...  factorization (LRTF-DFR) method [14] , non-local meets global (NG-meet) method [15] , and L1HyMixDe [16] ), by minimizing the rank of the underlying clean HSI (in low-rank matrix recovery (LRMR) method  ... 
arXiv:2109.08879v1 fatcat:3n5itohl6rb5lgs2vaq6o3i3xi

Bayesian Approach in a Learning-Based Hyperspectral Image Denoising Framework

Hazique Aetesam, Suman Kumar Maji, Hussein Yahia
2021 IEEE Access  
Zhang, “Hyperspectral image denoising pp. 1077–1080. using local low-rank matrix recovery and global spatial–spectral total [43] J.  ...  rank regularization for hyperspectral image denoising,” IEEE Transactions [44] A. Mahendran and A.  ... 
doi:10.1109/access.2021.3137656 fatcat:f32vzzaz4nb3jin7vnrazlmtp4
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