Decomposable Nonlocal Tensor Dictionary Learning for Multispectral Image Denoising

Peng, Yi; Meng, Deyu; Zhao, Xu; Gao, Chenqiang; Yang, Yi; Zhang, Biao

doi:10.1109/cvpr.2014.377

Cited by 311 publications

(206 citation statements)

References 23 publications

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“…Some studies also focus on the tensor extension of SRC/dictionary learning. Tensor-based dictionary learning methods are proposed in [43][44][45], while the compressed sensing is extended to multidimensional scenario in [46][47][48]. However, to the best of our knowledge, no research has been found regarding the tensor-based SRC and dictionary learning for HSI classification.…”

Section: Introductionmentioning

confidence: 99%

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Tensor Block-Sparsity Based Representation for Spectral-Spatial Hyperspectral Image Classification

Liu

2016

Remote Sensing

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Abstract:Recently, sparse representation has yielded successful results in hyperspectral image (HSI) classification. In the sparse representation-based classifiers (SRCs), a more discriminative representation that preserves the spectral-spatial information can be exploited by treating the HSI as a whole entity. Based on this observation, a tensor block-sparsity based representation method is proposed for spectral-spatial classification of HSI in this paper. Unlike traditional vector/matrix-based SRCs, the proposed method consists of tensor block-sparsity based dictionary learning and class-dependent block sparse representation. By naturally regarding the HSI cube as a third-order tensor, small local patches centered at the training samples are extracted from the HSI to maintain the structural information. All the patches are then partitioned into a number of groups, on which a dictionary learning model is constructed with a tensor block-sparsity constraint. A test sample is also expressed as a small local patch and the block sparse representation is then performed in a class-wise manner to take advantage of the class label information. Finally, the category of the test sample is determined by using the minimal residual. Experimental results of two real-world HSIs show that our proposed method greatly improves the classification performance of SRC.

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Section: Introductionmentioning

confidence: 99%

“…In this paper, we propose a tensor block-sparsity based representation method [43,47,48] for spectral-spatial classification of HSI. This method consists of two important steps, tensor block-sparsity based dictionary learning and class-dependent block sparse representation.…”

Section: Introductionmentioning

confidence: 99%

Tensor Block-Sparsity Based Representation for Spectral-Spatial Hyperspectral Image Classification

Liu

2016

Remote Sensing

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“…They yield digital images with around 1 million pixels, each associated with hundreds of spectral channels. Sparse matrix factorization has been widely used on these data for image classification [41,42] and denoising [43,44]. All methods rely on the extraction of full-band patches representing a local image neighborhood with all channels included.…”

Section: ) Hyperspectral Imagingmentioning

confidence: 99%

Stochastic Subsampling for Factorizing Huge Matrices

Mensch

Mairal

Thirion

et al. 2018

IEEE Trans. Signal Process.

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Abstract-We present a matrix-factorization algorithm that scales to input matrices with both huge number of rows and columns. Learned factors may be sparse or dense and/or nonnegative, which makes our algorithm suitable for dictionary learning, sparse component analysis, and non-negative matrix factorization. Our algorithm streams matrix columns while subsampling them to iteratively learn the matrix factors. At each iteration, the row dimension of a new sample is reduced by subsampling, resulting in lower time complexity compared to a simple streaming algorithm. Our method comes with convergence guarantees to reach a stationary point of the matrix-factorization problem. We demonstrate its efficiency on massive functional Magnetic Resonance Imaging data (2 TB), and on patches extracted from hyperspectral images (103 GB). For both problems, which involve different penalties on rows and columns, we obtain significant speed-ups compared to state-of-the-art algorithms.

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“…Nonlocal self-similarity [30][31][32] is a patch-based useful prior, which means that, for a given local patch in one image, there are many patches similar to it. Motivated by [33], separating X into a set of image patches Ω = {X n ∈ R b×b×B } P p=1 (where b is the patch size, P is the number of 3D patches with overlap), and by performing block matching [34], a group of patches that is most similar to each patch X p can be extracted.…”

Section: Nonlocal Low-rank Tensor Approximationmentioning

confidence: 99%

Hyperspectral Image Super-Resolution via Nonlocal Low-Rank Tensor Approximation and Total Variation Regularization

et al. 2017

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Hyperspectral image (HSI) possesses three intrinsic characteristics: the global correlation across spectral domain, the nonlocal self-similarity across spatial domain, and the local smooth structure across both spatial and spectral domains. This paper proposes a novel tensor based approach to handle the problem of HSI spatial super-resolution by modeling such three underlying characteristics. Specifically, a noncovex tensor penalty is used to exploit the former two intrinsic characteristics hidden in several 4D tensors formed by nonlocal similar patches within the 3D HSI. In addition, the local smoothness in both spatial and spectral modes of the HSI cube is characterized by a 3D total variation (TV) term. Then, we develop an effective algorithm for solving the resulting optimization by using the local linear approximation (LLA) strategy and the alternative direction method of multipliers (ADMM). A series of experiments are carried out to illustrate the superiority of the proposed approach over some state-of-the-art approaches.

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Decomposable Nonlocal Tensor Dictionary Learning for Multispectral Image Denoising

Cited by 311 publications

References 23 publications

Tensor Block-Sparsity Based Representation for Spectral-Spatial Hyperspectral Image Classification

Tensor Block-Sparsity Based Representation for Spectral-Spatial Hyperspectral Image Classification

Stochastic Subsampling for Factorizing Huge Matrices

Hyperspectral Image Super-Resolution via Nonlocal Low-Rank Tensor Approximation and Total Variation Regularization

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