scholarly journals Infrared thermographic inspection of 3D hybrid aluminium-CFRP composite using different spectral bands and new unsupervised probabilistic low-rank component factorization model

2021 ◽  
pp. 102561
Author(s):  
Henrique Fernandes ◽  
Hai Zhang ◽  
Steven Quirin ◽  
Jue Hu ◽  
Michael Schwarz ◽  
...  
Keyword(s):  
Low Rank ◽  
Spectral Bands ◽  
Factorization Model ◽  
Cfrp Composite

scholarly journals Multispectral image denoising using sparse and graph Laplacian Tucker decomposition

2020 ◽  
Vol 6 (3) ◽  
pp. 319-331
Author(s):  
Xiaoce Wu ◽  
Bingyin Zhou ◽  
Qingyun Ren ◽  
Wei Guo
Keyword(s):  
Prior Knowledge ◽  
Image Denoising ◽  
Graph Laplacian ◽  
Noise Removal ◽  
Multispectral Image ◽  
Low Rank ◽  
Tucker Decomposition ◽  
Spectral Bands ◽  
Global Correlation ◽  
Ill Posed

Abstract Multispectral image denoising is a basic problem whose results affect subsequent processes such as target detection and classification. Numerous approaches have been proposed, but there are still many challenges, particularly in using prior knowledge of multispectral images, which is crucial for solving the ill-posed problem of noise removal. This paper considers both non-local self-similarity in space and global correlation in spectrum. We propose a novel low-rank Tucker decomposition model for removing the noise, in which sparse and graph Laplacian regularization terms are employed to encode this prior knowledge. It can jointly learn a sparse and low-rank representation while preserving the local geometrical structure between spectral bands, so as to better capture simultaneously the correlation in spatial and spectral directions. We adopt the alternating direction method of multipliers to solve the resulting problem. Experiments demonstrate that the proposed method outperforms the state-of-the-art, such as cube-based and tensor-based methods, both quantitatively and qualitatively.

scholarly journals Relative Weighted Feature Space for Dimensionality Reduction and Classification of Hyperspectral Images

2017 ◽  
Author(s):  
Seyyed Ali Ahmadi ◽  
Nasser Mehrshad ◽  
Seyyed Mohammad Razavi
Keyword(s):  
Data Analysis ◽  
Dimension Reduction ◽  
Arbitrary Dimension ◽  
Feature Space ◽  
Hyperspectral Images ◽  
Low Rank ◽  
Similarity Function ◽  
Data Set ◽  
Weighting Matrix ◽  
Spectral Bands

Containing hundreds of spectral bands (features), hyperspectral images (HSIs) have high ability in discrimination of land cover classes. Traditional HSIs data processing methods consider the same importance for all bands in the original feature space (OFS), while different spectral bands play different roles in identification of samples of different classes. In order to explore the relative importance of each feature, we learn a weighting matrix and obtain the relative weighted feature space (RWFS) as an enriched feature space for HSIs data analysis in this paper. To overcome the difficulty of limited labeled samples which is common case in HSIs data analysis, we extend our method to semisupervised framework. To transfer available knowledge to unlabeled samples, we employ graph based clustering where low rank representation (LRR) is used to define the similarity function for graph. After construction the RWFS, any arbitrary dimension reduction method and classification algorithm can be employed in RWFS. The experimental results on two well-known HSIs data set show that some dimension reduction algorithms have better performance in the new weighted feature space.

scholarly journals Robust Asymmetric Bayesian Adaptive Matrix Factorization

2017 ◽  
Author(s):  
Xin Guo ◽  
Boyuan Pan ◽  
Deng Cai ◽  
Xiaofei He
Keyword(s):  
Real World ◽  
Matrix Factorization ◽  
State Of The Art ◽  
Low Rank ◽  
Data Sets ◽  
Real World Application ◽  
Factorization Model ◽  
Rank Matrix ◽  
Wide Range ◽  
Low Rank Matrix

Low rank matrix factorizations(LRMF) have attracted much attention due to its wide range of applications in computer vision, such as image impainting and video denoising. Most of the existing methods assume that the loss between an observed measurement matrix and its bilinear factorization follows symmetric distribution, like gaussian or gamma families. However, in real-world situations, this assumption is often found too idealized, because pictures under various illumination and angles may suffer from multi-peaks, asymmetric and irregular noises. To address these problems, this paper assumes that the loss follows a mixture of Asymmetric Laplace distributions and proposes robust Asymmetric Laplace Adaptive Matrix Factorization model(ALAMF) under bayesian matrix factorization framework. The assumption of Laplace distribution makes our model more robust and the asymmetric attribute makes our model more flexible and adaptable to real-world noise. A variational method is then devised for model inference. We compare ALAMF with other state-of-the-art matrix factorization methods both on data sets ranging from synthetic and real-world application. The experimental results demonstrate the effectiveness of our proposed approach.

scholarly journals Moreau-Enhanced Total Variation and Subspace Factorization for Hyperspectral Denoising

2020 ◽  
Vol 12 (2) ◽  
pp. 212
Author(s):  
Yanhong Yang ◽  
Shengyong Chen ◽  
Jianwei Zheng
Keyword(s):  
Total Variation ◽  
Computational Cost ◽  
Projection Matrix ◽  
Low Rank ◽  
Noise Levels ◽  
Unified Framework ◽  
Visual Effects ◽  
Spectral Correlation ◽  
Spectral Bands ◽  
Augmented Lagrange Multiplier

Hyperspectral images (HSIs) denoising aims at recovering noise-free images from noisy counterparts to improve image visualization. Recently, various prior knowledge has attracted much attention in HSI denoising, e.g., total variation (TV), low-rank, sparse representation, and so on. However, the computational cost of most existing algorithms increases exponentially with increasing spectral bands. In this paper, we fully take advantage of the global spectral correlation of HSI and design a unified framework named subspace-based Moreau-enhanced total variation and sparse factorization (SMTVSF) for multispectral image denoising. Specifically, SMTVSF decomposes an HSI image into the product of a projection matrix and abundance maps, followed by a ‘Moreau-enhanced’ TV (MTV) denoising step, i.e., a nonconvex regularizer involving the Moreau envelope mechnisam, to reconstruct all the abundance maps. Furthermore, the schemes of subspace representation penalizing the low-rank characteristic and ℓ 2 , 1 -norm modelling the structured sparse noise are embedded into our denoising framework to refine the abundance maps and projection matrix. We use the augmented Lagrange multiplier (ALM) algorithm to solve the resulting optimization problem. Extensive results under various noise levels of simulated and real hypspectral images demonstrate our superiority against other competing HSI recovery approaches in terms of quality metrics and visual effects. In addition, our method has a huge advantage in computational efficiency over many competitors, benefiting from its removal of most spectral dimensions during iterations.

scholarly journals An Efficient Tensor Completion Method Combining Matrix Factorization and Smoothness

2021 ◽  
Vol 2021 ◽  
pp. 1-13
Author(s):  
Leiming Tang ◽  
Xunjie Cao ◽  
Weiyang Chen ◽  
Changbo Ye
Keyword(s):  
Matrix Factorization ◽  
Low Complexity ◽  
Low Rank ◽  
Total Variation Regularization ◽  
Tensor Completion ◽  
Alternating Direction ◽  
Factorization Model ◽  
Efficiency And Effectiveness ◽  
The Matrix ◽  
Accelerate Convergence

In this paper, the low-complexity tensor completion (LTC) scheme is proposed to improve the efficiency of tensor completion. On one hand, the matrix factorization model is established for complexity reduction, which adopts the matrix factorization into the model of low-rank tensor completion. On the other hand, we introduce the smoothness by total variation regularization and framelet regularization to guarantee the completion performance. Accordingly, given the proposed smooth matrix factorization (SMF) model, an alternating direction method of multiple- (ADMM-) based solution is further proposed to realize the efficient and effective tensor completion. Additionally, we employ a novel tensor initialization approach to accelerate convergence speed. Finally, simulation results are presented to confirm the system gain of the proposed LTC scheme in both efficiency and effectiveness.

Causal Probabilistic Network and Power Spectral Estimation Used in Sleep Stage Classification

1997 ◽  
Vol 36 (04/05) ◽  
pp. 41-46
Author(s):  
A. Kjaer ◽  
W. Jensen ◽  
T. Dyrby ◽  
L. Andreasen ◽  
J. Andersen ◽  
...  
Keyword(s):  
Sleep Stage ◽  
Spectral Estimation ◽  
Interrater Agreement ◽  
Probabilistic Networks ◽  
Probabilistic Network ◽  
Spectral Bands ◽  
Power Spectral ◽  
Stage Classification ◽  
Sleep Stage Classification ◽  
The Brain

Abstract.A new method for sleep-stage classification using a causal probabilistic network as automatic classifier has been implemented and validated. The system uses features from the primary sleep signals from the brain (EEG) and the eyes (AOG) as input. From the EEG, features are derived containing spectral information which is used to classify power in the classical spectral bands, sleep spindles and K-complexes. From AOG, information on rapid eye movements is derived. Features are extracted every 2 seconds. The CPN-based sleep classifier was implemented using the HUGIN system, an application tool to handle causal probabilistic networks. The results obtained using different training approaches show agreements ranging from 68.7 to 70.7% between the system and the two experts when a pooled agreement is computed over the six subjects. As a comparison, the interrater agreement between the two experts was found to be 71.4%, measured also over the six subjects.

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