Nonlocal Tensor Sparse Representation and Low-Rank Regularization for Hyperspectral Image Compressive Sensing Reconstruction
Open Access
- 19 January 2019
- journal article
- research article
- Published by MDPI AG in Remote Sensing
- Vol. 11 (2), 193
- https://doi.org/10.3390/rs11020193
Abstract
Hyperspectral image compressive sensing reconstruction (HSI-CSR) is an important issue in remote sensing, and has recently been investigated increasingly by the sparsity prior based approaches. However, most of the available HSI-CSR methods consider the sparsity prior in spatial and spectral vector domains via vectorizing hyperspectral cubes along a certain dimension. Besides, in most previous works, little attention has been paid to exploiting the underlying nonlocal structure in spatial domain of the HSI. In this paper, we propose a nonlocal tensor sparse and low-rank regularization (NTSRLR) approach, which can encode essential structured sparsity of an HSI and explore its advantages for HSI-CSR task. Specifically, we study how to utilize reasonably the -based sparsity of core tensor and tensor nuclear norm function as tensor sparse and low-rank regularization, respectively, to describe the nonlocal spatial-spectral correlation hidden in an HSI. To study the minimization problem of the proposed algorithm, we design a fast implementation strategy based on the alternative direction multiplier method (ADMM) technique. Experimental results on various HSI datasets verify that the proposed HSI-CSR algorithm can significantly outperform existing state-of-the-art CSR techniques for HSI recovery.
Keywords
Funding Information
- National Natural Science Foundation of China (61371152, 61771391)
This publication has 54 references indexed in Scilit:
- Compressive Sensing via Nonlocal Low-Rank RegularizationIEEE Transactions on Image Processing, 2014
- An Operational Approach to PCA+JPEG2000 Compression of Hyperspectral ImageryIEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2013
- An efficient augmented Lagrangian method with applications to total variation minimizationComputational Optimization and Applications, 2013
- Bregmanized Nonlocal Regularization for Deconvolution and Sparse ReconstructionSIAM Journal on Imaging Sciences, 2010
- Distributed Optimization and Statistical Learning via the Alternating Direction Method of MultipliersFoundations and Trends® in Machine Learning, 2010
- Tensor Decompositions and ApplicationsSIAM Review, 2009
- For most large underdetermined systems of linear equations the minimal 𝓁1‐norm solution is also the sparsest solutionCommunications on Pure and Applied Mathematics, 2006
- An iterative thresholding algorithm for linear inverse problems with a sparsity constraintCommunications on Pure and Applied Mathematics, 2004
- Image Quality Assessment: From Error Visibility to Structural SimilarityIEEE Transactions on Image Processing, 2004
- Clustered dpcm for the lossless compression of hyperspectral imagesIEEE Transactions on Geoscience and Remote Sensing, 2003