Accurate quaternion fractional-order pseudo-Jacobi–Fourier moments

Wang, Xiangyang; Zhang, Yuyang; Tian, Jialin; Niu, Panpan; Yang, Heng

doi:10.1007/s10044-022-01071-6

Cited by 4 publications

(5 citation statements)

References 48 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…We computed the average values of BER of 18 images shown in Figure 3 . Then compared the obtained results with the latest zero-watermarking algorithms [ 20 , 22 , 23 , 26 , 30 , 32 ], where the comparison results are reported in Table 5 . We notice that the proposed algorithm achieved the lowest BER and exhibits higher-robustness than the existing zero-watermarking algorithms [ 20 , 22 , 23 , 26 , 30 , 32 ].…”

Section: Resultsmentioning

confidence: 99%

“…The first is the generation, while the second is the verification. We calculate the execution time for the 18 carrier images (512 × 512) and the watermarks (32 × 32) using the proposed and the existing methods [ 20 , 22 , 23 , 26 , 30 , 32 ]. The average computation times are reported in Table 6 .…”

Section: Resultsmentioning

confidence: 99%

“…The proposed zero watermark algorithm requires much less time than the existing algorithms [ 20 , 22 , 23 , 26 , 30 , 32 ].…”

Section: Resultsmentioning

confidence: 99%

“…In [ 31 ], the authors designed FoRHFMs-based lossless copyright protection for medical grayscale images. In [ 32 ], Wang and his co-authors derived QFPJFMs for color images and then applied these moments in a zero-watermarking algorithm.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Robust Zero-Watermarking of Color Medical Images Using Multi-Channel Gaussian-Hermite Moments and 1D Chebyshev Chaotic Map

Khafaga

Karim

Darwish

et al. 2022

Sensors

View full text Add to dashboard Cite

Copyright protection of medical images is a vital goal in the era of smart healthcare systems. In recent telemedicine applications, medical images are sensed using medical imaging devices and transmitted to remote places for screening by physicians and specialists. During their transmission, the medical images could be tampered with by intruders. Traditional watermarking methods embed the information in the host images to protect the copyright of medical images. The embedding destroys the original image and cannot be applied efficiently to images used in medicine that require high integrity. Robust zero-watermarking methods are preferable over other watermarking algorithms in medical image security due to their outstanding performance. Most existing methods are presented based on moments and moment invariants, which have become a prominent method for zero-watermarking due to their favorable image description capabilities and geometric invariance. Although moment-based zero-watermarking can be an effective approach to image copyright protection, several present approaches cannot effectively resist geometric attacks, and others have a low resistance to large-scale attacks. Besides these issues, most of these algorithms rely on traditional moment computation, which suffers from numerical error accumulation, leading to numerical instabilities, and time consumption and affecting the performance of these moment-based zero-watermarking techniques. In this paper, we derived multi-channel Gaussian–Hermite moments of fractional-order (MFrGHMs) to solve the problems. Then we used a kernel-based method for the highly accurate computation of MFrGHMs to solve the computation issue. Then, we constructed image features that are accurate and robust. Finally, we presented a new zero-watermarking scheme for color medical images using accurate MFrGHMs and 1D Chebyshev chaotic features to achieve lossless copyright protection of the color medical images. We performed experiments where their outcomes ensure the robustness of the proposed zero-watermarking algorithms against various attacks. The proposed zero-watermarking algorithm achieves a good balance between robustness and imperceptibility. Compared with similar existing algorithms, the proposed algorithm has superior robustness, security, and time computation.

show abstract

Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

“…The proposed zero watermark algorithm requires much less time than the existing algorithms [ 20 , 22 , 23 , 26 , 30 , 32 ].…”

Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Robust Zero-Watermarking of Color Medical Images Using Multi-Channel Gaussian-Hermite Moments and 1D Chebyshev Chaotic Map

Khafaga

Karim

Darwish

et al. 2022

Sensors

View full text Add to dashboard Cite

show abstract

“…The fractional-order derivative extends the integer-order derivative to allow arbitrary orders [22,23,24,25]. Three main definitions are used to define the fractional-order derivative: the Riemann-Liouville (R-L), the Grunwald-Letnikov (G-L) and the Caputo definitions.…”

Section: Fractional-order Gradientmentioning

confidence: 99%

Blind Image Deblurring: When Patch-wise Minimal Pixels Prior Meets Fractional-Order Method

Wu,

Wan,

Feng

et al. 2024

Preprint

View full text Add to dashboard Cite

Blind image deblurring is a challenging issue in image processing. In blind image deblurring, the typical approach involves iteratively estimating both the blur kernel and latent image until convergence to the blur kernel of the observed image is achieved. Recently, several approaches have been attempted to develop a sophisticated regularization to obtain the clean image. However, existing methods often struggle to effectively handle ringing artifacts and local blur. To overcome these limitations, we introduce a fractional-order variational model. This model alleviates the ringing artifacts through the selection of an optimal derivative. Subsequently, to refine the latent image further, we leverage the local prior, namely Patch-wise Minimal Pixels (PMP) prior. Since the PMP prior of clean image blocks is much sparser than that of blurred ones, it is capable of discriminating between clean and blurred image blocks. We illustrate the effective integration of the fractional-order operations and the PMP prior within our proposed approach. Moreover, the convergence of our algorithm has been proven as the values of the objective function monotonically decrease. Extensive experiments on different datasets demonstrate the superiority of the proposed method compared with other methods in terms of reconstruction quality for blind deblurring.

show abstract

Accurate and fast quaternion fractional-order Franklin moments for color image analysis

Zhu,

Huang,

Gao

et al. 2024

Digital Signal Processing

View full text Add to dashboard Cite

Accurate quaternion fractional-order pseudo-Jacobi–Fourier moments

Cited by 4 publications

References 48 publications

Robust Zero-Watermarking of Color Medical Images Using Multi-Channel Gaussian-Hermite Moments and 1D Chebyshev Chaotic Map

Robust Zero-Watermarking of Color Medical Images Using Multi-Channel Gaussian-Hermite Moments and 1D Chebyshev Chaotic Map

Blind Image Deblurring: When Patch-wise Minimal Pixels Prior Meets Fractional-Order Method

Accurate and fast quaternion fractional-order Franklin moments for color image analysis

Contact Info

Product

Resources

About