Image Fusion Based Enhancement of Nondestructive Evaluation Systems

Elshafiey, Ibrahim; Algarni, Ayed; Alkanhal, Majeed A. S.

doi:10.5772/15462

Cited by 6 publications

(9 citation statements)

References 39 publications

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“…Although the authors in reference 66 dealt with this kind of fusion and satisfactory experimental results were reported, the authors did not provide any useful information about the level of complexity nor did they provide any evaluation of their fusion results. To the best of our knowledge, there is currently no study that outlines the level of these difficulties in quantitative terms except some random descriptions in the literature 22,71 with limited details.…”

Section: Optimization-based Approachesmentioning

confidence: 99%

“…22,73,81 In reference, 81 for example, an MRA-based approach was used to fuse several MF-ECT images to improve the SNR and the visibility of the deep-lying flaws and achieved good results. In reference, 22 the authors used three different methods, namely, the local-energy comparison technique, the image averaging method, and the region-based method, to combine the shearographic, X-ray, and UT images. Although the authors did not make a final conclusion linking the effect of the fusion process to their ability to fully inspect the test structure, they made two important conclusions that seemed to support their findings.…”

Section: Multiresolution Analysismentioning

confidence: 99%

“…16 In 2001, the book Applications of NDT Data Fusion 17 was published followed by a second review article in 2007. 18 At this time, it appears NDE data fusion was becoming increasingly popular and several studies were published on this topic in journals 19,20 and conference proceedings 21,22 in the subsequent years. To date, most of the NDE data fusion techniques have gone beyond the earlier concepts 13,17 and they now provide greater opportunities for quantitative NDE, 23,24 owing to the advanced data processing techniques to enhance the signal-to-noise ratio (SNR) and the current technological advancements.…”

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confidence: 99%

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Critical insights into the state‐of‐the‐art NDE data fusion techniques for the inspection of structural systems

Nsengiyumva

Zhong

Luo

et al. 2021

Structural Contr & Hlth

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In recent years, the demand for reliable and accurate nondestructive evaluation (NDE) of structural systems has been growing and several nondestructive testing (NDT) techniques are being combined to address the issues of uncertainty in the testing results. However, the synergistic combination of multiple NDT techniques and the interpretation of the testing results require the ability to process and analyze data from several sources, a process that is referred to as "data fusion." The latter has received considerable attention since the beginning of the 21st century, thanks to the advances in sensor technologies and data acquisition systems. Also, the new era of "Big Data" where heterogeneous data are measured by different sensors and processed for a unified purpose is another factor contributing to this rapid development and the establishment of new NDT opportunities. This article reviews the recent studies involving NDE data fusion for the inspection of structures and examines the state-of-the-art mathematical expressions used in different fusion algorithms. Challenges in the handling of the NDE data and the application of fusion algorithms are also identified and discussed. A generic framework of applying NDE data fusion is described, and a roadmap for potential research prospects in NDE data fusion is provided.data fusion, diagnostic and prognostic health monitoring (DPHM), multisensor fusion and integration (MFI), nondestructive evaluation (NDE), structural health monitoring (SHM), structural systems | INTRODUCTIONEngineering structures are manufactured through multivariable processes and can easily develop defects during the manufacturing process and suffer from various issues (e.g., inappropriate usages, progressive aging, impact damage, excessive loading, and natural hazards such as earthquakes, hurricanes, etc.) during their service life. 1,2 To this end, several nondestructive evaluation (NDE) techniques have been devised (e.g., ultrasonic testing, infrared thermography, terahertz spectrometry, and imaging) to inspect these structures and their corresponding components to ensure they are free from defects and that their structural integrity is maintained throughout their service life. [1][2][3][4] At the manufacturing stage, NDE determines the structure's fitness to accomplish its intended function, and during the service stage, it determines the structure's health status, monitors the damage mechanism,

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Section: Optimization-based Approachesmentioning

confidence: 99%

Section: Multiresolution Analysismentioning

confidence: 99%

mentioning

confidence: 99%

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Critical insights into the state‐of‐the‐art NDE data fusion techniques for the inspection of structural systems

Nsengiyumva

Zhong

Luo

et al. 2021

Structural Contr & Hlth

View full text Add to dashboard Cite

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“…Data fusion techniques [18,19] were also reported for solving the NDE inverse problems. Most of the fusion algorithms rely on the signal processing data dis-regard to the data acquisition techniques [20][21][22][23][24][25][26]. Sequential Monte Carlo (SMC)-based technique was also reported to solve NDE inverse problems using single measurement mode data [1] and multiple measurement mode data [27].…”

Section: Introductionmentioning

confidence: 99%

Hybrid resampling scheme for particle filter‐based inversion

Zafar

Mairaj

Alam

et al. 2020

IET Science, Measurement & Technology

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“…With the numerous algorithms for image fusion, it is important to determine the quality of the image fusion process based on a set of standardized metrics [3,4,5]. Image fusion is an important component of many applications such as non-destructive evaluation [6] (e.g. eddy current and ultrasonic [7]), night vision [3] and target tracking [8] (e.g.…”

Section: Introductionmentioning

confidence: 99%

LANDSAT satellite image fusion metric assessment

Blasch

Liu

2011

Proceedings of the 2011 IEEE National Aerospace and Electronics Conference (NAECON)

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Image fusion is an important component of many applications such as inspection, night vision, medical diagnosis, and electro-optical (multispectral) targeting. In this paper, we build upon our previous results for image fusion metrics by applying the methods to satellite imagery versus night vision (visual and infrared imagery) and single-metric biomedical image fusion analysis. Multispectral examples include satellite imagery for land-cover analysis. In this paper, we explore a variety of metrics that have been used to quantify the performance of the fused image products. The exhaustive conclusive analysis over all possibilities is not realizable, so in this paper we group the various metrics into categories and demonstrate an application of the metrics to satellite LANDSAT imagery using an entropy-based image fusion method. From the many metrics analyzed, we found the Image Structural Similarity Based (ISSB) metrics are most useful.

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Image Fusion Based Enhancement of Nondestructive Evaluation Systems

Cited by 6 publications

References 39 publications

Critical insights into the state‐of‐the‐art NDE data fusion techniques for the inspection of structural systems

Critical insights into the state‐of‐the‐art NDE data fusion techniques for the inspection of structural systems

Hybrid resampling scheme for particle filter‐based inversion

LANDSAT satellite image fusion metric assessment

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