Fractional Transform Methods in Complexity Analysis: Theory and Image Applications

A special issue of Fractal and Fractional (ISSN 2504-3110). This special issue belongs to the section "Complexity".

Deadline for manuscript submissions: 31 December 2026 | Viewed by 1475

Special Issue Editor

College of Computer Science and Technology, Taiyuan University of Technology, Jinzhong 030600, China
Interests: complexity and dynamical analysis; nonlinear analysis; complex network; image encryption; image hiding; image processing

Special Issue Information

Dear Colleagues,

The integration of fractional transform methods with complexity analysis has become a pivotal, fast-evolving research frontier at the intersection of applied mathematics, signal processing, and computer science. It enriches complexity quantification theory—using fractional transforms’ non-local, memory-dependent properties to capture intricate dynamics—and advances image applications (enhancing feature extraction, noise-robust analysis, etc.), with impacts on medical imaging (e.g., tumor characterization), remote sensing, and industrial defect detection.

This Special Issue requests high-quality papers covering the following: novel fractional transform paradigms (e.g., fractional Fourier/wavelet transforms) for complexity quantification; theoretical explorations of fractional transform-enhanced complexity metrics (e.g., entropy); image applications such as denoising, segmentation, and content security; comparative/validation studies of fractional vs. traditional methods in image scenarios. Cross-disciplinary studies on complexity analysis and applications (e.g., integrating with machine learning for intelligent image interpretation) are also encouraged. Comprehensive review articles on the field’s progress, challenges, and future directions will also be considered.

Dr. Hao Zhang
Guest Editor

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Keywords

  • complexity analysis
  • fractional theory
  • fractals theory
  • nonlinear dynamics
  • image encryption and coding
  • image compression
  • image watermarking and information hiding
  • image segmentation and processing
  • control and stability with complexities
  • AI methods in image processing

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Published Papers (2 papers)

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Research

26 pages, 12944 KB  
Article
A 5D Fractional-Order Memristive Neural Network for Satellite Image Encryption Using Dynamic DNA Encoding and Bidirectional Diffusion
by Jinghui Ding, Yanping Zhu, Weiquan Yin, Dazhe He, Fayu Wan and Gangyi Tu
Fractal Fract. 2026, 10(4), 216; https://doi.org/10.3390/fractalfract10040216 - 26 Mar 2026
Cited by 2 | Viewed by 579
Abstract
To address the high redundancy and weak security inherent in satellite image transmission, this paper proposes an image encryption algorithm founded on a novel five-dimensional fractional-order cosine memristive Hopfield neural network (5D-FOCMHNN). The constructed hyperchaotic system exhibits long-term memory and multistability, capable of [...] Read more.
To address the high redundancy and weak security inherent in satellite image transmission, this paper proposes an image encryption algorithm founded on a novel five-dimensional fractional-order cosine memristive Hopfield neural network (5D-FOCMHNN). The constructed hyperchaotic system exhibits long-term memory and multistability, capable of generating reconfigurable multi-scroll attractors. A multivariate bit-level scrambling strategy effectively disrupts pixel correlations using neuron state sequences. Furthermore, the system’s chaotic output dynamically governs DNA encoding rules, while a bidirectional diffusion mechanism ensures strong randomization and resistance to differential attacks. Comprehensive experiments demonstrate that the 5D-FOCMHNN-based scheme provides a key space of 2256, has an information entropy approaching the ideal value of 8, and exhibits robust resilience against cropping, noise, and statistical cryptanalysis, thereby providing a highly secure solution for satellite image transmission. Full article
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14 pages, 4513 KB  
Article
Two-Dimensional Quaternion Fractional Fourier Transform: Definition and Probabilistic Analysis
by Muhammad Adnan Samad, Zhuhuang Zhou, Yuanqing Xia, Saima Siddiqui, Mohra Zayed and Mohammad Younus Bhat
Fractal Fract. 2026, 10(2), 89; https://doi.org/10.3390/fractalfract10020089 - 27 Jan 2026
Viewed by 562
Abstract
This article presents a detailed study of the two-dimensional quaternion fractional Fourier transform (2D QFRFT) and investigates its role in the probabilistic analysis of quaternion-valued signals. The 2D formulation is constructed by applying fractional Fourier transforms independently along each spatial dimension, thereby extending [...] Read more.
This article presents a detailed study of the two-dimensional quaternion fractional Fourier transform (2D QFRFT) and investigates its role in the probabilistic analysis of quaternion-valued signals. The 2D formulation is constructed by applying fractional Fourier transforms independently along each spatial dimension, thereby extending classical 2D Fourier and fractional Fourier frameworks to the quaternion domain. Key analytical properties of the 2D QFRFT, including linearity, shift behavior, differentiation, convolution, and energy relations, are summarized based on existing results in the literature. Furthermore, the transform is employed to define and analyze fundamental probabilistic quantities, such as expected value and normalized probability distributions, within the 2D quaternion fractional transform domain. These results provide a systematic 2D extension of existing quaternion transform-based probabilistic models and offer a clear theoretical foundation for the representation and analysis of 2D quaternion-valued signals in non-commutative settings. Full article
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