scholarly journals Toward Super-Resolution Image Construction Based on Joint Tensor Decomposition

2020 ◽  
Vol 12 (16) ◽  
pp. 2535
Author(s):  
Xiaoxu Ren ◽  
Liangfu Lu ◽  
Jocelyn Chanussot
Keyword(s):  
Three Dimensional ◽  
Super Resolution ◽  
Matrix Decomposition ◽  
Tensor Decomposition ◽  
Low Rank ◽  
Superior Performance ◽  
Original Image ◽  
Practical Applications ◽  
Structure Information ◽  
Degenerate Operators

In recent years, fusing hyperspectral images (HSIs) and multispectral images (MSIs) to acquire super-resolution images (SRIs) has been in the spotlight and gained tremendous attention. However, some current methods, such as those based on low rank matrix decomposition, also have a fair share of challenges. These algorithms carry out the matrixing process for the original image tensor, which will lose the structure information of the original image. In addition, there is no corresponding theory to prove whether the algorithm can guarantee the accurate restoration of the fused image due to the non-uniqueness of matrix decomposition. Moreover, degenerate operators are usually unknown or difficult to estimate in some practical applications. In this paper, an image fusion method based on joint tensor decomposition (JTF) is proposed, which is more effective and more applicable to the circumstance that degenerate operators are unknown or tough to gauge. Specifically, in the proposed JTF method, we consider SRI as a three-dimensional tensor and redefine the fusion problem with the decomposition issue of joint tensors. We then formulate the JTF algorithm, and the experimental results certify the superior performance of the proposed method in comparison to the current popular schemes.

scholarly journals Joint Spatial-Spectral Smoothing in a Minimum-Volume Simplex for Hyperspectral Image Super-Resolution

2019 ◽  
Vol 10 (1) ◽  
pp. 237 ◽  
Author(s):  
Fei Ma ◽  
Feixia Yang ◽  
Ziliang Ping ◽  
Wenqin Wang
Keyword(s):  
Hyperspectral Image ◽  
Structural Information ◽  
Three Dimensional ◽  
Super Resolution ◽  
Matrix Decomposition ◽  
Minimum Volume ◽  
Structure Information ◽  
Fusion Methods ◽  
Image Super Resolution ◽  
Spectral Smoothing

The limitations of hyperspectral sensors usually lead to coarse spatial resolution of acquired images. A well-known fusion method called coupled non-negative matrix factorization (CNMF) often amounts to an ill-posed inverse problem with poor anti-noise performance. Moreover, from the perspective of matrix decomposition, the matrixing of remotely-sensed cubic data results in the loss of data’s structural information, which causes the performance degradation of reconstructed images. In addition to three-dimensional tensor-based fusion methods, Craig’s minimum-volume belief in hyperspectral unmixing can also be utilized to restore the data structure information for hyperspectral image super-resolution. To address the above difficulties simultaneously, this article incorporates the regularization of joint spatial-spectral smoothing in a minimum-volume simplex, and spatial sparsity—into the original CNMF, to redefine a bi-convex problem. After the convexification of the regularizers, the alternating optimization is utilized to decouple the regularized problem into two convex subproblems, which are then reformulated by separately vectorizing the variables via vector-matrix operators. The alternating direction method of multipliers is employed to split the variables and yield the closed-form solutions. In addition, in order to solve the bottleneck of high computational burden, especially when the size of the problem is large, complexity reduction is conducted to simplify the solutions with constructed matrices and tensor operators. Experimental results illustrate that the proposed algorithm outperforms state-of-the-art fusion methods, which verifies the validity of the new fusion approach in this article.

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.

Generating High-Quality Air-Coupled Ultrasonic Images for Wooden Material Characterization by Single Image Super-Resolution

2019 ◽  
Vol 34 (03) ◽  
pp. 2054008
Author(s):  
Lujun Lin ◽  
Yiming Fang ◽  
Xiaochen Du ◽  
Zhu Zhou
Keyword(s):  
Image Quality ◽  
Super Resolution ◽  
Visual Assessment ◽  
Wood Products ◽  
Superior Performance ◽  
Single Image ◽  
High Quality ◽  
Practical Applications ◽  
Image Super Resolution ◽  
Single Image Super Resolution

As the practical applications in other fields, high-resolution images are usually expected to provide a more accurate assessment for the air-coupled ultrasonic (ACU) characterization of wooden materials. This paper investigated the feasibility of applying single image super-resolution (SISR) methods to recover high-quality ACU images from the raw observations that were constructed directly by the on-the-shelf ACU scanners. Four state-of-the-art SISR methods were applied to the low-resolution ACU images of wood products. The reconstructed images were evaluated by visual assessment and objective image quality metrics, including peak signal-to-noise-ratio and structural similarity. Both qualitative and quantitative evaluations indicated that the substantial improvement of image quality can be yielded. The results of the experiments demonstrated the superior performance and high reproducibility of the method for generating high-quality ACU images. Sparse coding based super-resolution and super-resolution convolutional neural network (SRCNN) significantly outperformed other algorithms. SRCNN has the potential to act as an effective tool to generate higher resolution ACU images due to its flexibility.

scholarly journals Three-dimensional cellular structures with negative Poisson's ratio and negative compressibility properties

2012 ◽  
Vol 468 (2146) ◽  
pp. 3121-3138 ◽  
Author(s):  
Joseph N. Grima ◽  
Roberto Caruana-Gauci ◽  
Daphne Attard ◽  
Ruben Gatt
Keyword(s):  
Mechanical Properties ◽  
Poisson’S Ratio ◽  
Three Dimensional ◽  
Cellular Systems ◽  
Poisson's Ratio ◽  
Superior Performance ◽  
Negative Poisson’S Ratio ◽  
Practical Applications ◽  
Negative Poisson's Ratio ◽  
Design And Manufacture

A three-dimensional cellular system that may be made to exhibit some very unusual but highly useful mechanical properties, including negative Poisson's ratio (auxetic), zero Poisson's ratio, negative linear and negative area compressibility, is proposed and discussed. It is shown that such behaviour is scale-independent and may be obtained from particular conformations of this highly versatile system. This model may be used to explain the auxetic behaviour in auxetic foams and in other related cellular systems; such materials are widely known for their superior performance in various practical applications. It may also be used as a blueprint for the design and manufacture of new man-made multifunctional systems, including auxetic and negative compressibility systems, which can be made to have tailor-made mechanical properties.

scholarly journals Compressed Sensing and Low-Rank Matrix Decomposition in Multisource Images Fusion

2014 ◽  
Vol 2014 ◽  
pp. 1-7 ◽  
Author(s):  
Kan Ren ◽  
Fuyuan Xu ◽  
Guohua Gu
Keyword(s):  
High Resolution ◽  
Compressive Sensing ◽  
Super Resolution ◽  
Matrix Decomposition ◽  
Fusion Rule ◽  
Low Rank ◽  
Signal Recovery ◽  
Rank Matrix ◽  
Low Rank Matrix ◽  
Fusion Scheme

We propose a novel super-resolution multisource images fusion scheme via compressive sensing and dictionary learning theory. Under the sparsity prior of images patches and the framework of the compressive sensing theory, the multisource images fusion is reduced to a signal recovery problem from the compressive measurements. Then, a set of multiscale dictionaries are learned from several groups of high-resolution sample image’s patches via a nonlinear optimization algorithm. Moreover, a new linear weights fusion rule is proposed to obtain the high-resolution image. Some experiments are taken to investigate the performance of our proposed method, and the results prove its superiority to its counterparts.

scholarly journals Blind Hyperspectral and Multispectral Image Fusion Using Coupled Non-Negative Tucker Tensor Decomposition

2021 ◽  
Author(s):  
Marzieh Zare
Keyword(s):  
Spatial Resolution ◽  
Hyperspectral Image ◽  
State Of The Art ◽  
Super Resolution ◽  
Tensor Decomposition ◽  
Multispectral Image ◽  
Tensor Factorization ◽  
Structure Information ◽  
Joint Structure ◽  
Art Methods

Fusing a low spatial resolution hyperspectral image (HSI) with a high spatial resolution multispectral image (MSI) to produce a fused high spatio-spectal resolution one, referred to as HSI super-resolution, has recently attracted increasing research interests. In this paper, a new method based on coupled non-negative tensor decomposition (CNTD) is proposed. The proposed method uses tucker tensor factorization for low resolution hyperspectral image (LR-HSI) and high resolution multispectral image (HR-MSI) under the constraint of non-negative tensor ecomposition (NTD). The conventional non-negative matrix factorization (NMF) method essentially loses spatio-spectral joint structure information when stacking a 3D data into a matrix form. On the contrary, in NMF-based methods, the spectral, spatial, or their joint structures must be imposed from outside as a constraint to well pose the NMF problem, The proposed CNTD method blindly brings the advantage of preserving the spatio-spectral joint structure of HSIs. In this paper, the NTD is imposed on the coupled tensor of HIS and MSI straightly. Hence the intrinsic spatio-spectral joint structure of HSI can be losslessly expressed and interdependently exploited. Furthermore, multilinear interactions of different modes of the HSIs can be exactly modeled by means of the core tensor of the Tucker tensor decomposition. The proposed method is completely straight forward and easy to implement. Unlike the other state-of-the-art methods, the complexity of the proposed CNTD method is quite linear with the size of the HSI cube. Compared with the state-of-the-art methods experiments on two well-known datasets, give promising results with lower complexity order.

scholarly journals Weather Radar Image Superresolution Using a Nonlocal Residual Network

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Haoxuan Yuan ◽  
Qiangyu Zeng ◽  
Jianxin He
Keyword(s):  
Large Scale ◽  
Low Frequency ◽  
Super Resolution ◽  
Weather Radar ◽  
Superior Performance ◽  
Residual Network ◽  
Structure Information ◽  
Radar Images ◽  
Radar Echo ◽  
High Level

Accurate and high-resolution weather radar images reflecting detailed structure information of radar echo are vital for analysis and forecast of extreme weather. Typically, this is performed by using interpolation schemes, which only use several neighboring data values for computational approximation to get the estimated value regardless of the large-scale context feature of weather radar images. Inspired by the striking performance of the convolutional neural network (CNN) applied in feature extraction and nonlocal self-similarity of weather radar images, we proposed a nonlocal residual network (NLRN) on the basis of CNN. The proposed network mainly consists of several nonlocal residual blocks (NLRB), which combine short skip connection (SSC) and nonlocal operation to train the deep network and capture large-scale context information. In addition, long skip connection (LSC) added in the network avoids learning low-frequency information, making the network focus on high-level features. Extensive experiments of ×2 and ×4 super-resolution reconstruction demonstrate that NLRN achieves superior performance in terms of both quantitative evaluation metrics and visual quality, especially for the reconstruction of the edge and detailed information of the weather radar echo.

scholarly journals Tensor Ring Decomposition with Rank Minimization on Latent Space: An Efficient Approach for Tensor Completion

2019 ◽  
Vol 33 ◽  
pp. 9151-9158 ◽  
Author(s):  
Longhao Yuan ◽  
Chao Li ◽  
Danilo Mandic ◽  
Jianting Cao ◽  
Qibin Zhao
Keyword(s):  
Model Selection ◽  
Computational Cost ◽  
Tensor Decomposition ◽  
Low Rank ◽  
Superior Performance ◽  
Tensor Completion ◽  
Real World Data ◽  
Latent Space ◽  
Decomposition Models ◽  
Value Decomposition

In tensor completion tasks, the traditional low-rank tensor decomposition models suffer from the laborious model selection problem due to their high model sensitivity. In particular, for tensor ring (TR) decomposition, the number of model possibilities grows exponentially with the tensor order, which makes it rather challenging to find the optimal TR decomposition. In this paper, by exploiting the low-rank structure of the TR latent space, we propose a novel tensor completion method which is robust to model selection. In contrast to imposing the low-rank constraint on the data space, we introduce nuclear norm regularization on the latent TR factors, resulting in the optimization step using singular value decomposition (SVD) being performed at a much smaller scale. By leveraging the alternating direction method of multipliers (ADMM) scheme, the latent TR factors with optimal rank and the recovered tensor can be obtained simultaneously. Our proposed algorithm is shown to effectively alleviate the burden of TR-rank selection, thereby greatly reducing the computational cost. The extensive experimental results on both synthetic and real-world data demonstrate the superior performance and efficiency of the proposed approach against the state-of-the-art algorithms.

scholarly journals Robust Tensor Decomposition via Orientation Invariant Tubal Nuclear Norms

2020 ◽  
Vol 34 (04) ◽  
pp. 6102-6109 ◽  
Author(s):  
Andong Wang ◽  
Chao Li ◽  
Zhong Jin ◽  
Qibin Zhao
Keyword(s):  
Estimation Error ◽  
Tensor Decomposition ◽  
Low Rank ◽  
Superior Performance ◽  
Orientation Sensitivity ◽  
Asymptotic Error ◽  
Rank Tensor ◽  
Tensor Recovery ◽  
Decomposition Models ◽  
Real World Datasets

Low-rank tensor recovery has been widely applied to computer vision and machine learning. Recently, tubal nuclear norm (TNN) based optimization is proposed with superior performance as compared to other tensor nuclear norms. However, one major limitation is its orientation sensitivity due to low-rankness strictly defined along tubal orientation and it cannot simultaneously model spectral low-rankness in multiple orientations. To this end, we introduce two new tensor norms called OITNN-O and OITNN-L to exploit multi-orientational spectral low-rankness for an arbitrary K-way (K ≥ 3) tensors. We further formulate two robust tensor decomposition models via the proposed norms and develop two algorithms as the solutions. Theoretically, we establish non-asymptotic error bounds which can predict the scaling behavior of the estimation error. Experiments on real-world datasets demonstrate the superiority and effectiveness of the proposed norms.

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