Denoising hyperspectral images with non-white noise based on tensor decomposition

2016 ◽  
Author(s):  
Xuefeng Liu ◽  
Yuping Feng ◽  
Yingge Li ◽  
Ximei Liu ◽  
Wencang Zhao ◽  
...  
Keyword(s):  
White Noise ◽  
Tensor Decomposition ◽  
Hyperspectral Images

Compressive Sensing of Noisy 3-D Images Based on Threshold Selection

2018 ◽  
Vol 22 (1) ◽  
pp. 70-75 ◽  
Author(s):  
Qingzhu Wang ◽  
Mengying Wei ◽  
Yihai Zhu ◽  
◽  
Keyword(s):  
Sensor Networks ◽  
Medical Imaging ◽  
Compressive Sensing ◽  
Processing Speed ◽  
Tensor Decomposition ◽  
Hyperspectral Images ◽  
Video Sequences ◽  
Threshold Selection ◽  
Reconstruction Accuracy ◽  
Pseudo Inverse

Compressive sensing (CS) of high-order data such as hyperspectral images, medical imaging, video sequences, and multi-sensor networks is certainly a hot issue after the emergence of tensor decomposition. Actually, the reconstruction accuracy with current algorithms is not ideal in some cases of noise. In this paper, we propose a new method that can recover noisy 3-D images from a reduced set of compressive measurements. First, multi-way compressive measurements are performed using Gaussian random matrices. Second, the mapping relationship between the variance of noise and the reconstruction threshold is found. Finally, the original images are recovered through reconstruction of pseudo inverse based on threshold selection. We experimentally demonstrate that the proposed method outperforms other similar methods in both reconstruction accuracy (within a range of the compression ratios and different variances of noise) and processing speed.

scholarly journals Hyperspectral Super-Resolution Via Joint Regularization of Low-Rank Tensor Decomposition

2021 ◽  
Vol 13 (20) ◽  
pp. 4116
Author(s):  
Meng Cao ◽  
Wenxing Bao ◽  
Kewen Qu
Keyword(s):  
High Resolution ◽  
Hyperspectral Image ◽  
Super Resolution ◽  
Tensor Decomposition ◽  
Hyperspectral Data ◽  
Hyperspectral Images ◽  
Low Rank ◽  
Superior Performance ◽  
Rank Tensor ◽  
Image Super Resolution

The hyperspectral image super-resolution (HSI-SR) problem aims at reconstructing the high resolution spatial–spectral information of the scene by fusing low-resolution hyperspectral images (LR-HSI) and the corresponding high-resolution multispectral image (HR-MSI). In order to effectively preserve the spatial and spectral structure of hyperspectral images, a new joint regularized low-rank tensor decomposition method (JRLTD) is proposed for HSI-SR. This model alleviates the problem that the traditional HSI-SR method, based on tensor decomposition, fails to adequately take into account the manifold structure of high-dimensional HR-HSI and is sensitive to outliers and noise. The model first operates on the hyperspectral data using the classical Tucker decomposition to transform the hyperspectral data into the form of a three-mode dictionary multiplied by the core tensor, after which the graph regularization and unidirectional total variational (TV) regularization are introduced to constrain the three-mode dictionary. In addition, we impose the l1-norm on core tensor to characterize the sparsity. While effectively preserving the spatial and spectral structures in the fused hyperspectral images, the presence of anomalous noise values in the images is reduced. In this paper, the hyperspectral image super-resolution problem is transformed into a joint regularization optimization problem based on tensor decomposition and solved by a hybrid framework between the alternating direction multiplier method (ADMM) and the proximal alternate optimization (PAO) algorithm. Experimental results conducted on two benchmark datasets and one real dataset show that JRLTD shows superior performance over state-of-the-art hyperspectral super-resolution algorithms.

PLTD: Patch-Based Low-Rank Tensor Decomposition for Hyperspectral Images

2017 ◽  
Vol 19 (1) ◽  
pp. 67-79 ◽  
Author(s):  
Bo Du ◽  
Mengfei Zhang ◽  
Lefei Zhang ◽  
Ruimin Hu ◽  
Dacheng Tao
Keyword(s):  
Tensor Decomposition ◽  
Hyperspectral Images ◽  
Low Rank ◽  
Rank Tensor

scholarly journals Tensor decomposition of hyperspectral images to study autofluorescence in age-related macular degeneration

2019 ◽  
Vol 56 ◽  
pp. 96-109 ◽  
Author(s):  
Neel Dey ◽  
Sungmin Hong ◽  
Thomas Ach ◽  
Yiannis Koutalos ◽  
Christine A. Curcio ◽  
...  
Keyword(s):  
Macular Degeneration ◽  
Tensor Decomposition ◽  
Age Related Macular Degeneration ◽  
Hyperspectral Images ◽  
Age Related
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