Super Resolution for Multi-Sources Image Stream Data Using Smooth and Sparse Tensor Completion and Its Applications in Data Acquisition of Additive Manufacturing

Shen, Bo; Wang, Rongxuan; Law, Adrian Wing‐Keung; Kamath, Rakesh R; Choo, Hahn; Kong, Zhenyu

doi:10.1080/00401706.2021.1905074

Cited by 16 publications

(3 citation statements)

References 35 publications

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“…where e 2 2 is the fitting error; the first penalty term measures the smoothness in θ through the roughness matrix A; the second penalty term describes the sparsity in θ a ; and τ 1 , τ 2 > 0 are regularization coefficients to be tuned. The idea of smooth and sparse decomposition is further extended to the literature of tensor completion [25].…”

Section: B Related Workmentioning

confidence: 99%

Robust Tensor Decomposition Based Background/Foreground Separation in Noisy Videos and Its Applications in Additive Manufacturing

Shen

Kamath

Choo

et al. 2023

IEEE Trans. Automat. Sci. Eng.

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Background/foreground separation is one of the most fundamental tasks in computer vision, especially for video data. Robust PCA (RPCA) and its tensor extension, namely, Robust Tensor PCA (RTPCA), provide an effective framework for background/foreground separation by decomposing the data into low-rank and sparse components, which contain the background and the foreground (moving objects), respectively. However, in real-world applications, the video data is contaminated with noise. For example, in metal additive manufacturing (AM), the processed X-ray video to study melt pool dynamics is very noisy. RPCA and RTPCA are not able to separate the background, foreground, and noise simultaneously. As a result, the noise will contaminate the background or the foreground or both. There is a need to remove the noise from the background and foreground. To achieve the three terms decomposition, a smooth sparse Robust Tensor Decomposition (SS-RTD) model is proposed to decompose the data into static background, smooth foreground, and noise, respectively. Specifically, the static background is modeled by the low-rank tucker decomposition, the smooth foreground (moving objects) is modeled by the spatiotemporal continuity, which is enforced by the total variation regularization, and the noise is modeled by the sparsity, which is enforced by the 1 norm. An efficient algorithm based on alternating direction method of multipliers (ADMM) is implemented to solve the proposed model. Extensive experiments on both simulated and real data demonstrate that the proposed method significantly outperforms the state-of-the-art approaches for background/foreground separation in noisy cases.Note to Practitioners-This work is motivated by melt pool detection in metal additive manufacturing where the processed X-ray video from the monitoring system is very noisy. The objective is to recover the background with porosity defects and the foreground with melt pool in the presence of noise. Existing methods fail to separate the noise from the background and foreground since RPCA and RTPCA have only two components, which cannot explain the three components in the data. This paper puts forward a smooth sparse Robust Tensor Decomposition by decomposing the tensor data into lowrank, smooth, and sparse components, respectively. It is a highly effective method for background/foreground separation in noisy case. In the case studies on simulated video and X-ray data, the proposed method can handle non-additive noise, and even the case of high noise-ratio. In the proposed algorithm, there is only one tuning parameter λ. Based on the case studies, our method achieves satisfying performance by taking any λ ∈ [0.2, 1] with anisotropic total variation regularization (q = {1}). That means the proposed method is a kind of "parameter-free"

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Section: B Related Workmentioning

confidence: 99%

Robust Tensor Decomposition Based Background/Foreground Separation in Noisy Videos and Its Applications in Additive Manufacturing

Shen

Kamath

Choo

et al. 2023

IEEE Trans. Automat. Sci. Eng.

Self Cite

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“…Flow-3D Weld Flow Science ( 2019) is a simulation software based on computational fluid dynamics (CFD). It provides powerful insights into laser welding processes such as electron beam melting (EBM) and selective laser melting (SLM) Gokuldoss et al (2017), which are representative powder bed fusion additive manufacturing for machine learning applications in quality and reliability Shen et al (2020Shen et al ( , 2021. When we use the Flow-3D Weld, the material of metal powder (for example, Ti6Al4V) and its raw material property (for example, fluid absorption rate (FAR) and thermal conductivity (TC)), as well as printing conditions (for example, laser power and dwell time), are the input to the software.…”

Section: Application 2: Raw Materials Properties Determination For Flow-3d Weldmentioning

confidence: 99%

Multi-Task Gaussian Process Upper Confidence Bound for Hyperparameter Tuning

Shen¹,

Gnanasambandam²,

Rongxuan³

et al. 2021

Preprint

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In many scientific and engineering applications, Bayesian optimization (BO) is a powerful tool for hyperparameter tuning of a machine learning model, materials design and discovery, etc. BO guides the choice of experiments in a sequential way to find a good combination of design points in as few experiments as possible. It can be formulated as a problem of optimizing a “black-box” function. Different from single-task Bayesian optimization, Multi-task Bayesian optimization is a general method to efficiently optimize multiple different but correlated “black-box” functions. The previous works in Multi-task Bayesian optimization algorithm queries a point to be evaluated for all tasks in each round of search, which is not efficient. For the case where different tasks are correlated, it is not necessary to evaluate all tasks for a given query point. Therefore, the objective of this work is to develop an algorithm for multi-task Bayesian optimization with automatic task selection so that only one task evaluation is needed per query round. Specifically, a new algorithm, namely, multi-task Gaussian process upper confidence bound (MT-GPUCB), is proposed to achieve this objective. The MT-GPUCB is a two-step algorithm, where the first step chooses which query point to evaluate, and the second step automatically selects the most informative task to evaluate. Under the bandit setting, a theoretical analysis is provided to show that our proposed MT-GPUCB is no-regret under some mild conditions. Our proposed algorithm is verified experimentally on a range of synthetic functions as well as real-world problems. The results clearly show the advantages of our query strategy for both design point and task.

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“…As data is often represented in the form of tensors, tensor completion is an essential topic to many fields. Examples of its applications include visual data recovery [30], [52], super-resolution [35], big data analytics [36], anomaly detection [43], traffic data analysis [7], [40], [45], neuroimaging [12], or even recommender systems and knowledge graph completion [3]. Therefore, many academic topics are closely connected to tensor completion, and its improvement could lead to myriad potential advances in other fields.…”

Section: Introductionmentioning

confidence: 99%

Low-Rank Tensor Completion With Generalized CP Decomposition and Nonnegative Integer Tensor Completion

Yuan¹

2023

Preprint

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The problem of tensor completion is important to many areas such as computer vision, data analysis, signal processing, etc. Previously, a category of methods known as low-rank tensor completion has been proposed and developed, involving the enforcement of low-rank structures on completed tensors. While such methods have been constantly improved, none have previously considered exploiting the numerical properties of tensor elements. This work attempts to construct a new methodological framework called GCDTC (Generalized CP Decomposition Tensor Completion) based on these properties. In this newly introduced framework, the CP Decomposition is reformulated as a Maximum Likelihood Estimate (MLE) problem, and generalized via the introduction of differing loss functions. The generalized decomposition is subsequently applied to low-rank tensor completion. Such loss functions can also be easily adjusted to consider additional factors in completion, such as smoothness, standardization, etc. An example of nonnegative integer tensor decomposition via the Poisson CP Decomposition is given to demonstrate the new methodology's potentials. Through experimentation with real-life data, it is confirmed that this method could produce results superior to current state-of-the-art methodologies. It is expected that the proposed notion would inspire a new set of tensor completion methods based on the generalization of decompositions, thus contributing to related fields.

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Super Resolution for Multi-Sources Image Stream Data Using Smooth and Sparse Tensor Completion and Its Applications in Data Acquisition of Additive Manufacturing

Cited by 16 publications

References 35 publications

Robust Tensor Decomposition Based Background/Foreground Separation in Noisy Videos and Its Applications in Additive Manufacturing

Robust Tensor Decomposition Based Background/Foreground Separation in Noisy Videos and Its Applications in Additive Manufacturing

Multi-Task Gaussian Process Upper Confidence Bound for Hyperparameter Tuning

Low-Rank Tensor Completion With Generalized CP Decomposition and Nonnegative Integer Tensor Completion

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