Nonlocal Similarity Based Nonnegative Tucker Decomposition for Hyperspectral Image Denoising

Bai, Xiao; Xu, Fan; Zhou, Lei; Xiao, Yan; Bai, Lu; Zhou, Jun

doi:10.1109/jstars.2018.2791718

Cited by 64 publications

(18 citation statements)

References 43 publications

(47 reference statements)

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“…The patches in one group exhibit perfect mutual similarity, i.e., the non-local self-similarity. Then, the non-local base methods [10], [21], [31], [37] use this prior to denoise a group patches together. In addition, it has been shown in [10], [32] that non-local methods go beyond TV based methods when dealing with many inverse imaging problems.…”

Section: The Proposed Hsi Denoising Modelmentioning

confidence: 99%

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

et al. 2020

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Hyperspectral images (HSIs) are usually corrupted by various noises during the image acquisition process, e.g., Gaussian noise, impulse noise, stripes, deadlines and many others. Such complex noise severely degrades the data quality, reduces the interpretation accuracy of HSIs, and restricts the subsequent HSI applications. 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. Specifically, we first divide HSIs into local overlapping patches. Local rank-constrained low-rank matrix recovery is adopted to effectively separate the low-rank clean HSI patches from the sparse noise and a part of Gaussian noise, and to significantly preserve local structure and detail information in HSIs. Then the spatial non-local based denoiser is introduced to promote the non-local self-similarity and obviously depress the Gaussian noise. Without increasing the difficulty of solving optimization problems, we combine the local and non-local based methods into the Plug-and-Play framework, and develop an efficient algorithm for solving the proposed NLRPnP model by using the alternating direction method of multipliers method. Finally, several experiments are conducted in both simulated and real data conditions to illustrate the better performance of the proposed NLRPnP model than the existing state-of-the-art denoising models. INDEX TERMS Hyperspectral images, denoising, plug-and-play framework, local low-rank matrix recovery, non-local regularization.

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Section: The Proposed Hsi Denoising Modelmentioning

confidence: 99%

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

et al. 2020

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“…Chen et al [19] utilized the tensor-ring decomposition model to construct high-order tensors, which improves the ability for noisy HS images. Moreover, other priors, such as non-negativity [20], are also incorporated with the tensor model for HS image denoising.…”

Section: Introductionmentioning

confidence: 99%

Hyperspectral Image Denoising Using 3-D Geometrical Kernel With Local Similarity Prior

Zhang

Sun

et al. 2021

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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The majority of existing hyperspectral (HS) image denoising methods exploit local similarity in HS images by rearranging them into the matrix or vector forms. As the typical 3D data, the inherent spatial and spectral properties in HS images should be simultaneously explored for denoising. Therefore, a 3D geometrical kernel (3DGK) is developed in this paper to describe the local structure. The proposed method assumes that the pixel can be represented by other pixels within a 3D block efficiently owing to the local similarity with adjacent positions. Then, the HS image is modeled by the 3D kernel regression with L1-norm constraint, in which the local similarity is captured by the proposed 3DGK. To efficiently compute the parameters in 3DGK, geometrical structures, such as scale, shape, and orientation in the 3D block, are estimated from the gradient information approximately. Finally, the noises are effectively removed while preserving the structures in HS images. Moreover, experimental results on simulated and real datasets demonstrate that the performance of 3DGK is better than those of the methods based on local similarity prior.

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“…The spatial non-local similarity suggests that similar patches inside a HSI can be grouped and denoised together. The related methods [1,10,13,16,14,31,39,50] denoise the HSIs via group matching of full band patches (FBPs, stacked by patches at the same location of HSI over all bands) and low-rank denoising of each non-local FBP group (NLFBPG). These methods have achieved state-of-the-art performance.…”

Section: Introductionmentioning

confidence: 99%

Non-Local Meets Global: An Integrated Paradigm for Hyperspectral Denoising

Wei

Yao

et al. 2019

2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

124

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Non-local low-rank tensor approximation has been developed as a state-of-the-art method for hyperspectral image (HSI) denoising. Unfortunately, while their denoising performance benefits little from more spectral bands, the running time of these methods significantly increases. In this paper, we claim that the HSI lies in a global spectral low-rank subspace, and the spectral subspaces of each full band patch groups should lie in this global low-rank subspace. This motivates us to propose a unified spatialspectral paradigm for HSI denoising. As the new model is hard to optimize, An efficient algorithm motivated by alternating minimization is developed. This is done by first learning a low-dimensional orthogonal basis and the related reduced image from the noisy HSI. Then, the non-local low-rank denoising and iterative regularization are developed to refine the reduced image and orthogonal basis, respectively. Finally, the experiments on synthetic and both real datasets demonstrate the superiority against the stateof-the-art HSI denoising methods.

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Nonlocal Similarity Based Nonnegative Tucker Decomposition for Hyperspectral Image Denoising

Cited by 64 publications

References 43 publications

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

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

Hyperspectral Image Denoising Using 3-D Geometrical Kernel With Local Similarity Prior

Non-Local Meets Global: An Integrated Paradigm for Hyperspectral Denoising

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