scholarly journals Hyperspectral Image Denoising Based on Nonconvex Low-Rank Tensor Approximation and lp Norm Regularization

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Li Bo ◽  
Luo Xuegang ◽  
Lv Junrui
Keyword(s):  
Image Denoising ◽  
Hyperspectral Image ◽  
Structural Information ◽  
Real Data ◽  
Optimization Method ◽  
Nuclear Norm ◽  
Low Rank ◽  
Phase Congruency ◽  
Structure Information ◽  
Lp Norm

A new nonconvex smooth rank approximation model is proposed to deal with HSI mixed noise in this paper. The low-rank matrix with Laplace function regularization is used to approximate the nuclear norm, and its performance is superior to the nuclear norm regularization. A new phase congruency lp norm model is proposed to constrain the spatial structure information of hyperspectral images, to solve the phenomenon of “artificial artifact” in the process of hyperspectral image denoising. This model not only makes use of the low-rank characteristic of the hyperspectral image accurately, but also combines the structural information of all bands and the local information of the neighborhood, and then based on the Alternating Direction Method of Multipliers (ADMM), an optimization method for solving the model is proposed. The results of simulation and real data experiments show that the proposed method is more effective than the competcing state-of-the-art denoising methods.

scholarly journals Low-Rank and Spectral-Spatial Sparse Unmixing for Hyperspectral Remote Sensing Imagery

2021 ◽  
Vol 2021 ◽  
pp. 1-14
Author(s):  
Fan Li
Keyword(s):  
Hyperspectral Image ◽  
Structural Information ◽  
Real Data ◽  
Hyperspectral Data ◽  
Spectral Unmixing ◽  
Low Rank ◽  
Superior Performance ◽  
Dimensional Structure ◽  
Spatial Continuity ◽  
Spatial Weights

Sparse unmixing is an important technique for hyperspectral data analysis. Most sparse unmixing algorithms underutilize the spatial and spectral information of the hyperspectral image, which is unfavourable for the accuracy of endmember identification and abundance estimation. We propose a new spectral unmixing method based on the low-rank representation model and spatial-weighted collaborative sparsity, aiming to exploit structural information in both the spatial and spectral domains for unmixing. The spatial weights are incorporated into the collaborative sparse regularization term to enhance the spatial continuity of the image. Meanwhile, the global low-rank constraint is employed to maintain the spatial low-dimensional structure of the image. The model is solved by the well-known alternating direction method of multiplier, in which the abundance coefficients and the spatial weights are updated iteratively in the inner and outer loops, respectively. Experimental results obtained from simulation and real data reveal the superior performance of the proposed algorithm on spectral unmixing.

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

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 102935-102946
Author(s):  
Guxi Wang ◽  
Hongwei Han ◽  
Emmanuel John M. Carranza ◽  
Si Guo ◽  
Ke Guo ◽  
...  
Keyword(s):  
Image Denoising ◽  
Prior Information ◽  
Hyperspectral Image ◽  
Low Rank ◽  
Information Constraints ◽  
Sparse Prior
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