Digital watermarking scheme for colour remote sensing image based on quaternion wavelet transform and tensor decomposition

Li, Dongshuang; Che, Xiaoyu; Luo, Wen; Hu, Yong; Wang, Yannan; Yu, Zhaoyuan; Yuan, Linwang

doi:10.1002/mma.5668

Cited by 15 publications

(8 citation statements)

References 41 publications

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“…Qi et al [81] constructed a damage localization index based on lower and upper (LU) decomposition of the proportional flexibility matrix and applied it to detect damage in composite beams. Li et al [82] applied the quickly orthogonal and right (QR) decomposition to the proportional flexibility matrix of a structure under environmental excitation to construct the indicators for the localization of the damage. They used model condensation technology to overcome the problem of incomplete measurement of the flexibility matrix.…”

Section: Assessing Damage By Decomposing the Flexibilitymentioning

confidence: 99%

Structural Damage Detection Based on Static and Dynamic Flexibility: A Review and Comparative Study

Peng,

Yang,

Qin

et al. 2023

Coatings

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Material damage in structures must be detected in a timely manner to prevent engineering accidents. Damage detection based on structural flexibility has attracted widespread attention in recent years due to its simplicity and practicality. This article provides a detailed overview of damage detection methods based on structural flexibility. Depending on the calculation method and data used, flexibility-based methods can be divided into the following categories: flexibility difference, flexibility derivative index, flexibility sensitivity, flexibility decomposition, static flexibility, and combinations of flexibility with other methods. The basic principles and main calculation formulas of various flexibility methods are explained, and their advantages and disadvantages are analyzed. For the method using flexibility difference, the advantage is that the calculation is very simple and does not require the construction of a finite element model of the structure. The disadvantage is that it requires the measurement of modal data of the intact structure, and this method cannot quantitatively assess the degree of damage. For the method using the flexibility derivative index, the advantage is that it only requires the modal data of the damaged structure to locate the damage, but this method is particularly sensitive to noise in the data and is prone to misjudgment. For methods based on flexibility sensitivity and flexibility decomposition, the advantage is that they can simultaneously obtain the location and degree of damage in the structure, but the disadvantage is that they require the establishment of accurate finite element models in advance. Static flexibility methods can compensate for the shortcomings of dynamic flexibility methods, but they usually affect the normal use of the structure during static testing. Combining flexibility-based methods with advanced intelligent algorithms and other methods can further improve their accuracy and efficiency in identifying structural damage. Finally, this article discusses the challenges that have not yet been solved among damage detection methods based on structural flexibility.

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Section: Assessing Damage By Decomposing the Flexibilitymentioning

confidence: 99%

Structural Damage Detection Based on Static and Dynamic Flexibility: A Review and Comparative Study

Peng,

Yang,

Qin

et al. 2023

Coatings

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“…Literature [19] proposed a technique based on scale-invariant feature transformation to improve the accuracy of descriptor matching by histogram weighting strategy. Literature [20] proposed a robust non-blind watermarking scheme for color remote sensing images constructed based on quaternion wavelet transform and tensor decomposition to be used for preserving detailed host image features and improving watermarking perception, and the feasibility of the proposed scheme was verified through simulation experiments. Literature [21] considers the elimination method as a key part of image preprocessing, and the wavelet transform is utilized to denoise the image in order to enhance the recovery of the image signal.…”

Section: Relevant Studiesmentioning

confidence: 99%

Wavelet transform-based characterization of printing ink penetration depth and image phase dissimilarity

Liu,

Fan,

Shi

et al. 2023

Applied Mathematics and Nonlinear Sciences

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In this paper, continuous wavelet transform and discrete wavelet transform are used to detect transient anomalies entrained in normal information and to demonstrate their components. Multi-scale analysis of wavelet transform, Haar wavelet basis and multi-scale edge detection algorithms are utilized to determine the modal extreme points and identify the edge points for faster and more accurate extraction of edge features of the image. In order to further validate the applicability and feasibility of wavelet transform for printing images and to determine the quality inspection criteria based on ink penetration depth and image phase anisotropy, MATLAB software is utilized to perform simulation tests. The results show that the wavelet transform can remove the noise generated by uneven illumination and printing background during the printing process and can detect the edges of the printing image with an error accuracy of ±0.063mm and meet the error correction accuracy of <0.4mm as required by the printing standard. The experiments verify the feasibility of the wavelet transform, which can characterize the depth of penetration of the printing ink and the image anisotropy and provides a theoretical basis for improving the quality of printing. The experiment confirms that wavelet transform can be used to measure printing ink penetration depth and image anisotropy, giving a theoretical basis for improving printing quality.

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“…This method is resistant to compression and geometric attacks. Li et al [23] used the quaternion wavelet transform (QWT) to transform the remote sensing images. The most stable coefficients were then filtered as the watermark embedding domain by tensor decomposition.…”

Section: Introductionmentioning

confidence: 99%

An adaptive watermarking for remote sensing images based on maximum entropy and discrete wavelet transformation

2024

KSII TIIS

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Most frequency-domain remote sensing image watermarking algorithms embed watermarks at random locations, which have negative impact on the watermark invisibility. In this study, we propose an adaptive watermarking scheme for remote sensing images that considers the information complexity to select where to embed watermarks to improve watermark invisibility without affecting algorithm robustness. The scheme converts remote sensing images from RGB to YCbCr color space, performs two-level DWT on luminance 𝑌𝑌, and selects the high frequency coefficient of the low frequency component ( 𝐻𝐻𝐻𝐻 2 𝑌𝑌 ) as the watermark embedding domain. To achieve adaptive embedding, 𝐻𝐻𝐻𝐻 2 𝑌𝑌 is divided into several 8*8 blocks, the entropy of each sub-block is calculated, and the block with the maximum entropy is chosen as the watermark embedding location. During embedding phase, the watermark image is also decomposed by two-level DWT, and the resulting high frequency coefficient (𝐻𝐻𝐻𝐻 2 𝑊𝑊 ) is then embedded into the block with maximum entropy using 𝛼𝛼-blending. The experimental results show that the watermarked remote sensing images have high fidelity, indicating good invisibility. Under varying degrees of geometric, cropping, filtering, and noise attacks, the proposed watermarking can always extract high identifiable watermark images. Moreover, it is extremely stable and impervious to attack intensity interference.

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Digital watermarking scheme for colour remote sensing image based on quaternion wavelet transform and tensor decomposition

Cited by 15 publications

References 41 publications

Structural Damage Detection Based on Static and Dynamic Flexibility: A Review and Comparative Study

Structural Damage Detection Based on Static and Dynamic Flexibility: A Review and Comparative Study

Wavelet transform-based characterization of printing ink penetration depth and image phase dissimilarity

An adaptive watermarking for remote sensing images based on maximum entropy and discrete wavelet transformation

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