Digital Signal Processing最新文献_第7页

Towards point cloud geometry compression via global-local and multi-scale feature learning 基于全局-局部和多尺度特征学习的点云几何压缩

IF 3 3区工程技术 Q2 ENGINEERING, ELECTRICAL & ELECTRONIC

Digital Signal Processing

Pub Date : 2026-01-13 DOI: 10.1016/j.dsp.2026.105913

Yihan Wang , Yongfang Wang , Zhijun Fang , Tengyao Cui

Existing Point Cloud Geometry Compression (PCGC) methods often inadequately handle non-uniform point density and fail to fully exploit multi-scale contextual features, limiting their efficiency and reconstruction quality. To bridge this gap, we argue that an effective solution must jointly addresses local geometric adaptation and the aggregation of multi-scale contextual features. Accordingly, we propose a novel PCGC method, consisting of Global-Local Feature Extraction Network (GLFE-Net), Multi-scale Feature Enhancement Network (MFE-Net), and Coordinates Reconstruction based on Offset (CRO). The GLFE-Net incorporates Local Adaptive Density (LAD) to address the non-uniform density distribution and Global-Local Context Differential (GLCD) module to fuse local and global features. The MFE-Net employs the Feature Extraction based on Offset-attention (FEO) module to enhance the feature expression ability, and utilizes the Multi-scale Semantics Fusion (MSF) module to optimize the multi-scale feature fusion. The CRO module utilizes the learnable offset mechanism for high-fidelity reconstruction. Experimental results demonstrate that our method achieves significant improvements, with Peak Signal-to-Noise Ratio (PSNR) gains of up to 29.25 dB (D1) and 27.31 dB (D2) over the existing PCGC methods. This work provides an effective solution for high performance PCGC method by jointly addressing the key challenges of density adaptation and multi-scale feature learning.

现有的点云几何压缩（PCGC）方法往往不能充分处理非均匀点密度，不能充分利用多尺度上下文特征，限制了其效率和重建质量。为了弥补这一差距，我们认为一个有效的解决方案必须同时解决局部几何适应和多尺度上下文特征的聚集。为此，我们提出了一种新的PCGC方法，包括全局局部特征提取网络（GLFE-Net）、多尺度特征增强网络（MFE-Net）和基于偏移量的坐标重建（CRO）。GLFE-Net采用局部自适应密度（LAD）来解决密度分布不均匀的问题，采用全局-局部上下文差分（GLCD）模块来融合局部和全局特征。MFE-Net采用基于偏移注意力的特征提取（FEO）模块来增强特征表达能力，并利用多尺度语义融合（MSF）模块来优化多尺度特征融合。CRO模块利用可学习偏移机制实现高保真重建。实验结果表明，我们的方法取得了显著的改进，与现有的PCGC方法相比，峰值信噪比（PSNR）增益高达29.25 dB （D1）和27.31 dB （D2）。该工作通过共同解决密度自适应和多尺度特征学习的关键挑战，为高性能PCGC方法提供了有效的解决方案。

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引用次数: 0

Trainable joint time-vertex fractional Fourier transform 可训练联合时顶点分数傅里叶变换

IF 3 3区工程技术 Q2 ENGINEERING, ELECTRICAL & ELECTRONIC

Digital Signal Processing

Pub Date : 2026-01-13 DOI: 10.1016/j.dsp.2026.105909

Ziqi Yan , Zhichao Zhang

To address the limitations of the graph fractional Fourier transform (GFRFT) Wiener filtering and the traditional joint time-vertex fractional Fourier transform (JFRFT) Wiener filtering, this study proposes a filtering method based on the hyper-differential form of the JFRFT. The gradient backpropagation mechanism is employed to establish the adaptive selection of transform order pair and filter coefficients. First, leveraging the hyper-differential form of the GFRFT and the fractional Fourier transform, the hyper-differential form of the JFRFT is constructed and its properties are analyzed. Second, time-varying graph signals are divided into dynamic graph sequences of equal span along the temporal dimension. A spatiotemporal joint representation is then established through vectorized reorganization, followed by the joint time-vertex Wiener filtering. Furthermore, by rigorously proving the differentiability of the transform orders, both the transform orders and filter coefficients are embedded as learnable parameters within a neural network architecture. Through gradient backpropagation, their synchronized iterative optimization is achieved, constructing a parameters-adaptive learning filtering framework. This method leverages a model-driven approach to learn the optimal transform order pair and filter coefficients. Experimental results indicate that the proposed framework improves the time-varying graph signals denoising performance, while reducing the computational burden of the traditional grid search strategy.

针对图分数阶傅里叶变换（GFRFT）维纳滤波和传统联合时间顶点分数阶傅里叶变换（JFRFT）维纳滤波的局限性，提出了一种基于JFRFT超微分形式的滤波方法。利用梯度反向传播机制建立了变换阶对和滤波系数的自适应选择。首先，利用GFRFT的超微分形式和分数阶傅里叶变换，构造了JFRFT的超微分形式并分析了其性质。其次，将时变图信号沿时间维划分为等跨度的动态图序列；然后通过向量化重组建立时空联合表示，然后进行联合时间-顶点维纳滤波。此外，通过严格证明变换阶数的可微性，将变换阶数和滤波系数作为可学习参数嵌入到神经网络结构中。通过梯度反向传播，实现了它们的同步迭代优化，构造了一个参数自适应学习滤波框架。该方法利用模型驱动的方法来学习最优变换阶对和过滤系数。实验结果表明，该框架在提高时变图信号去噪性能的同时，减少了传统网格搜索策略的计算量。

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引用次数: 0

CPMNet: an enhanced residual network for continuous phase modulation signal detection CPMNet：用于连续相位调制信号检测的增强残差网络

IF 3 3区工程技术 Q2 ENGINEERING, ELECTRICAL & ELECTRONIC

Digital Signal Processing

Pub Date : 2026-01-13 DOI: 10.1016/j.dsp.2026.105914

Yang He , Ning Cao , Hao Lu , Can Hu , Yajuan Guo

Continuous Phase Modulation (CPM) signals offer excellent spectral efficiency and constant envelope properties for wireless communications, but traditional detection methods suffer from prohibitive computational complexity. This paper presents CPMNet, a novel deep learning-based detection framework that addresses these limitations through an enhanced residual network architecture incorporating spatial attention mechanisms, multi-scale feature fusion, and bidirectional LSTM networks. CPMNet performs sequence-to-sequence detection without requiring channel estimation or equalization. Experimental results on Advanced Range Telemetry (ARTM) Tier 2 signals show performance varies with modulation complexity: while exhibiting 2–4 dB gaps compared to Maximum Likelihood Sequence Detection (MLSD) in high signal-to-noise ratio (SNR) AWGN channels for lower-order modulations, CPMNet maintains robust performance for high-order modulations where MLSD becomes impractical. In multipath fading channels, CPMNet significantly outperforms MLSD by 3–6 dB across various conditions, demonstrating superior resilience to channel impairments. The framework exhibits excellent generalization with only 1–2 dB degradation in unseen environments. Most critically, CPMNet maintains constant computational complexity regardless of CPM parameters, contrasting sharply with MLSD’s exponential complexity growth, making it particularly advantageous for high-order CPM signals that are computationally prohibitive for traditional methods.

连续相位调制（CPM）信号为无线通信提供了优异的频谱效率和恒定的包络特性，但传统的检测方法存在计算复杂度过高的问题。本文提出了一种新的基于深度学习的检测框架CPMNet，该框架通过一种增强的残差网络架构，结合空间注意机制、多尺度特征融合和双向LSTM网络，解决了这些限制。CPMNet执行序列到序列检测，而不需要信道估计或均衡。先进距离遥测（ARTM）第2层信号的实验结果表明，调制复杂性不同，性能也不同：在高信噪比（SNR） AWGN信道中，与最大似然序列检测（MLSD）相比，CPMNet在低阶调制中表现出2 - 4 dB的差距，而在MLSD变得不切实际的高阶调制中，CPMNet保持了强大的性能。在多径衰落信道中，CPMNet在各种条件下都明显优于MLSD 3-6 dB，显示出对信道损伤的优越恢复能力。该框架具有出色的泛化性能，在不可见的环境中只有1-2 dB的退化。最关键的是，无论CPM参数如何，CPMNet都保持恒定的计算复杂度，这与MLSD的指数复杂度增长形成鲜明对比，这使得CPMNet对传统方法难以计算的高阶CPM信号特别有利。

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引用次数: 0

Research on denoising of rolling bearing vibration signals based on the ISSA-VMD-JWTD method 基于ISSA-VMD-JWTD方法的滚动轴承振动信号去噪研究

IF 3 3区工程技术 Q2 ENGINEERING, ELECTRICAL & ELECTRONIC

Digital Signal Processing

Pub Date : 2026-01-12 DOI: 10.1016/j.dsp.2026.105912

Xuchao Bai , Yuxian Wang , Chunfang Zhang

This paper proposes a novel method based on an improved sparrow search algorithm, variational mode decomposition (VMD), and joint wavelet threshold denoising (ISSA-VMD-JWTD). During the optimization process, cubic mapping is utilized to initialize the sparrow population, while weighted kurtosis serves as the fitness function to optimize the critical parameters of Variational Mode Decomposition (VMD). In the decomposition and denoising procedure, the vibration signal is first decomposed using VMD with the optimal parameter combination, followed by adaptive selection of effective components to eliminate low-frequency noise. Subsequently, a multi-objective optimization mechanism is established to autonomously determine the optimal wavelet threshold parameters for different wavelet families (including dbN, symN, and coifN). Parallel secondary denoising is then performed on the reconstructed ISSA-VMD signal using the optimized parameters from each wavelet family. Finally, the denoising results from multiple wavelet families are integrated through a variance-based weighting strategy to produce the ultimate denoised signal. Experimental results demonstrate that the proposed method significantly enhances the signal-to-noise ratio of denoised signals compared to other denoising approaches, while exhibiting superior adaptability and robustness for rolling bearing vibration signals under diverse operating conditions.

本文提出了一种基于改进的麻雀搜索算法、变分模态分解（VMD）和联合小波阈值去噪（ISSA-VMD-JWTD）的新方法。在优化过程中，利用三次映射初始化麻雀种群，加权峰度作为适应度函数对变分模态分解（VMD）的关键参数进行优化。在分解去噪过程中，首先利用最优参数组合对振动信号进行VMD分解，然后自适应选择有效分量去除低频噪声。随后，建立多目标优化机制，自主确定不同小波族（包括dbN、symN和coifN）的最优小波阈值参数。然后利用每个小波族的优化参数对重构的ISSA-VMD信号进行并行二次去噪。最后，通过基于方差的加权策略对多个小波族的去噪结果进行综合，得到最终去噪信号。实验结果表明，与其他去噪方法相比，该方法显著提高了去噪信号的信噪比，同时对不同工况下的滚动轴承振动信号具有较好的适应性和鲁棒性。

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引用次数: 0

Time-series clustering algorithm based on common tightest neighbors and local embedding 基于公共最紧邻居和局部嵌入的时间序列聚类算法

IF 3 3区工程技术 Q2 ENGINEERING, ELECTRICAL & ELECTRONIC

Digital Signal Processing

Pub Date : 2026-01-12 DOI: 10.1016/j.dsp.2026.105895

Lei Gao, Taichang Tian, Luosheng Wen

Time-series clustering is an important method in data mining, which is widely used in various fields. However, the traditional clustering algorithms directly deal with the time-series data, which will lead to the serious issue of “dimensionality catastrophe”. It is an important method to capture the local features of time-series data by using the neighbor information. In this paper, we propose a hierarchical graph clustering algorithm (CTNG) based on common tightest neighbors(CTN), which is able to cluster various kinds of complex streaming data and noisy data by using the ratio of common tightest neighbors between data points to determine whether the edges are connected in the tightest neighbors graph(TNG) or not. In order to solve the issue of “dimension disaster”, combined with the local linear embedding algorithm (LLE), this paper proposes a time-series clustering algorithm based on LLE_CTNG, which can make full use of the local structure of the data to realize the dimensionality reduction and clustering. Through a large number of experiments, it is shown that the algorithm has superior and stable clustering performance, has certain advantages in running speed, and is robust to the number of the tightest neighbors parameter.

时间序列聚类是数据挖掘中的一种重要方法，广泛应用于各个领域。然而，传统的聚类算法直接处理时间序列数据，这将导致严重的“维数突变”问题。利用邻域信息捕捉时间序列数据的局部特征是一种重要的方法。本文提出了一种基于共同最紧邻居（CTN）的分层图聚类算法（CTNG），该算法能够利用数据点之间的共同最紧邻居比率来确定最紧邻居图（TNG）中的边缘是否连通，从而对各种复杂的流数据和噪声数据进行聚类。为了解决“维数灾难”问题，结合局部线性嵌入算法（LLE），本文提出了一种基于LLE_CTNG的时间序列聚类算法，该算法可以充分利用数据的局部结构实现降维聚类。通过大量实验表明，该算法具有优越而稳定的聚类性能，在运行速度上具有一定优势，并且对最紧密邻居参数的个数具有鲁棒性。

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引用次数: 0

A novel two-dimensional Wigner distribution framework via the quadratic phase Fourier transform with a non-separable kernel 基于二次相傅里叶变换的不可分核二维Wigner分布框架

IF 3 3区工程技术 Q2 ENGINEERING, ELECTRICAL & ELECTRONIC

Digital Signal Processing

Pub Date : 2026-01-11 DOI: 10.1016/j.dsp.2026.105896

Mukul Chauhan, Waseem Z. Lone, Amit K. Verma

This paper introduces a novel time–frequency distribution, referred to as the two-dimensional non-separable quadratic-phase Wigner distribution (2D-NSQPWD), formulated within the framework of the two-dimensional non-separable quadratic-phase Fourier transform (2D-NSQPFT). The proposed distribution extends the classical two-dimensional Wigner distribution (2D-WD) through a convolution-based formulation that incorporates the structural characteristics of the 2D-NSQPFT, thereby enabling an effective representation of complex, non-separable signal structures. We rigorously establish several key properties of the 2D-NSQPWD, including time and frequency shift invariance, marginal behavior, conjugate symmetry, convolution relations, and Moyal’s identity. The effectiveness of the distribution is demonstrated through its application to single-, bi-, and tri-component two-dimensional linear frequency-modulated (2D-LFM) signals. Finally, simulations show that the proposed transform exhibits superior performance in cross-term suppression and signal localization compared to existing transforms.

本文介绍了一种新的时频分布，即二维不可分二次相维格纳分布（2D-NSQPWD），该分布是在二维不可分二次相傅里叶变换（2D-NSQPFT）的框架内提出的。所提出的分布通过基于卷积的公式扩展了经典二维维格纳分布（2D-WD），该公式结合了2D-NSQPFT的结构特征，从而能够有效地表示复杂的、不可分离的信号结构。我们严格地建立了2D-NSQPWD的几个关键性质，包括时频移不变性、边缘行为、共轭对称性、卷积关系和Moyal恒等式。通过对单分量、双分量和三分量二维线性调频（2D-LFM）信号的应用，证明了该分布的有效性。最后，仿真结果表明，与现有变换相比，该变换在交叉项抑制和信号定位方面具有更好的性能。

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引用次数: 0

Correct estimation of higher-order spectra: From theoretical challenges to practical multi-channel implementation in SignalSnap 高阶频谱的正确估计：从理论挑战到SignalSnap的实际多通道实现

IF 3 3区工程技术 Q2 ENGINEERING, ELECTRICAL & ELECTRONIC

Digital Signal Processing

Pub Date : 2026-01-10 DOI: 10.1016/j.dsp.2026.105893

Markus Sifft, Armin Ghorbanietemad, Fabian Wagner, Daniel Hägele

Higher-order spectra (Brillinger’s polyspectra) offer powerful methods for solving critical problems in signal processing and data analysis. Despite their significant potential, their practical use has remained limited due to unresolved mathematical issues in spectral estimation, including the absence of unbiased and consistent estimators and the high computational cost associated with evaluating multidimensional spectra. Consequently, existing tools frequently produce artifacts-no existing software library correctly implements Brillinger’s cumulant-based trispectrum-or fail to scale effectively to real-world data volumes, leaving crucial applications like multi-detector spectral analysis largely unexplored.

In this paper, we revisit higher-order spectra from a modern perspective, addressing the root causes of their historical underuse. We reformulate higher-order spectral estimation using recently derived multivariate k-statistics, yielding unbiased and consistent estimators that eliminate spurious artifacts and precisely align with Brillinger’s theoretical definitions. Our methodology covers single- and multi-channel spectral analysis up to the bispectrum (third order) and trispectrum (fourth order), enabling robust investigations of inter-frequency coupling, non-Gaussian behavior, and time-reversal symmetry breaking. Additionally, we introduce quasi-polyspectra to uncover non-stationary, time-dependent higher-order features. We implement these new estimators in SignalSnap, an open-source GPU-accelerated library capable of efficiently analyzing datasets exceeding hundreds of gigabytes within minutes.

In applications such as continuous quantum measurements, SignalSnap’s rigorous estimators enable precise quantitative matching between experimental data and theoretical models. With detailed derivations and illustrative examples, this work provides the theoretical and computational foundation necessary for establishing higher-order spectra as a reliable, standard tool in modern signal analysis.

高阶谱（布里林格多谱）为解决信号处理和数据分析中的关键问题提供了强有力的方法。尽管它们具有巨大的潜力，但由于光谱估计中未解决的数学问题，包括缺乏无偏和一致的估计器以及与评估多维光谱相关的高计算成本，它们的实际应用仍然有限。因此，现有的工具经常产生工件——没有现有的软件库正确地实现Brillinger的基于累积量的三光谱——或者不能有效地扩展到现实世界的数据量，使得像多探测器光谱分析这样的关键应用在很大程度上没有被探索。在本文中，我们从现代的角度重新审视高阶光谱，解决其历史上未充分利用的根本原因。我们使用最近导出的多元k统计量重新制定高阶光谱估计，产生无偏和一致的估计，消除了虚假的工件，并精确地与Brillinger的理论定义对齐。我们的方法涵盖单通道和多通道频谱分析，直至双频谱（三阶）和三频谱（四阶），能够对频间耦合，非高斯行为和时间反转对称性破断进行稳健的研究。此外，我们引入了准多光谱来揭示非平稳的、时变的高阶特征。我们在SignalSnap中实现了这些新的估计器，SignalSnap是一个开源的gpu加速库，能够在几分钟内有效地分析超过数百gb的数据集。在连续量子测量等应用中，SignalSnap的严格估计器可以实现实验数据和理论模型之间的精确定量匹配。通过详细的推导和举例说明，本工作为建立高阶谱作为现代信号分析中可靠的标准工具提供了必要的理论和计算基础。

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引用次数: 0

A multi-stage path aggregation module for small object detection on drone-captured scenarios 用于无人机捕获场景下小目标检测的多阶段路径聚合模块

IF 3 3区工程技术 Q2 ENGINEERING, ELECTRICAL & ELECTRONIC

Digital Signal Processing

Pub Date : 2026-01-10 DOI: 10.1016/j.dsp.2026.105901

Wenyuan Fan , Xuemei Xu , Zhaohui Jiang , Zehan Zhu

Small object detection remains a critical challenge due to limited pixel representation and uneven spatial distribution. In the absence of sufficient contextual information, it is difficult to extract discriminative and complete features for accurate detection. By analyzing multi-scale feature fusion within modern detectors, we proposed a Multi-stage Path Aggregation module(MPAM) composed of the Parallel Residual Fusion Module(PRFM) and the Differential Path Channel Aggregation Module(DPCAM). Through decomposing the path aggregation operation into multiple stages, MPAM significantly enhanced the feature maps’ capacity to accommodate and process contextual information. PRFM captured texture and semantic information from the multi-scale feature maps through skip connections. Moreover, a channel branch was added to enable the dynamic distribution of attention weights across both the channel and spatial dimensions. DPCAM is proposed to balance channel and spatial information from different feature maps through channel expansion operation. Additionally, Deep-wise Partial Attention(DPA) is designed to enhance the ability of representing features for small objects within complex backgrounds by balancing weights between local and global information. Integrated into popular detectors, our method delivers consistent gains. Compared with yolov8s, mAP50:95 of our method improved by 3.7% on VisDrone and 3.2% on MS COCO, respectively. Experimental results validate the effectiveness of the proposed module in significantly enhancing small object detection accuracy.

由于像素表示有限和空间分布不均匀，小目标检测仍然是一个关键的挑战。在缺乏足够的上下文信息的情况下，很难提取出有区别的、完整的特征来进行准确的检测。在分析现代探测器多尺度特征融合的基础上，提出了一种由并行残差融合模块（PRFM）和差分路径通道聚合模块（DPCAM）组成的多阶段路径聚合模块（MPAM）。通过将路径聚合操作分解为多个阶段，MPAM显著增强了特征映射容纳和处理上下文信息的能力。PRFM通过跳跃连接从多尺度特征图中捕获纹理和语义信息。此外，还增加了一个通道分支，以实现注意力权重在通道和空间维度上的动态分布。DPCAM通过通道扩展运算来平衡来自不同特征映射的通道和空间信息。此外，深度部分注意（Deep-wise Partial Attention， DPA）通过平衡局部和全局信息之间的权重，增强了复杂背景中小目标的特征表示能力。集成到流行的检测器，我们的方法提供一致的增益。与yolov8s相比，该方法的mAP50:95在VisDrone和MS COCO上分别提高了3.7%和3.2%。实验结果验证了该模块的有效性，显著提高了小目标的检测精度。

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引用次数: 0

Design and hardware implementation of a dynamically variable chaotic stream cipher system with analog-Digital hybrid control and synchronization 模数混合控制与同步的动态可变混沌流密码系统的设计与硬件实现

IF 3 3区工程技术 Q2 ENGINEERING, ELECTRICAL & ELECTRONIC

Digital Signal Processing

Pub Date : 2026-01-10 DOI: 10.1016/j.dsp.2026.105904

Hao Ming , Hanping Hu , Jun Zheng

For chaotic cryptography to advance toward practical deployment, it is necessary to pay attention not only to the security issues of chaotic systems but also to problems such as the actual degradation of digital performance and system synchronization. Regarding the security of the chaotic system itself, its characteristic information (including parameters, the structure of coupled chaotic systems, etc.) provides critical entry points for attackers. If these characteristics remain static, chaotic cryptography becomes increasingly vulnerable to cryptanalysis. In this paper, a time-variant stream cipher based on a nondegenerate and coupled chaotic system is proposed. The analog-digital hybrid technique is employed to solve the dynamical degradation in the digital field, and digital adaptive pulse control for synchronization. The coupling structure, delay, and parameter of the coupled chaos are dynamically varied following a time-variant mechanism to enhance the security. The practical effectiveness is demonstrated by FPGA-FPAA collaborative hardware design, wherein an event-triggered synchronization scheme is also presented for hardware implementation. Experimental results and theoretical analyses show that the proposed cipher can provide high-quality and robust keystreams for wide cryptographic applications. The construction strategy and components of the proposed cryptosystem are beneficial to motivate chaotic cipher designs and applications.

混沌密码学要走向实际部署，不仅需要关注混沌系统的安全问题，还需要关注实际数字性能下降和系统同步等问题。就混沌系统本身的安全性而言，其特征信息（包括参数、耦合混沌系统的结构等）为攻击者提供了关键的切入点。如果这些特征保持不变，混沌密码术就会越来越容易受到密码分析的攻击。提出了一种基于非退化耦合混沌系统的时变流密码。采用模数混合技术解决了数字领域的动态退化问题，并采用数字自适应脉冲控制实现同步。耦合混沌的耦合结构、时延和参数按照时变机制动态变化，以提高安全性。通过FPGA-FPAA协同硬件设计验证了该方法的实际有效性，并提出了一种事件触发同步方案用于硬件实现。实验结果和理论分析表明，该算法能够为广泛的密码学应用提供高质量和鲁棒性的密钥流。所提出的密码系统的构造策略和组成有利于激励混沌密码的设计和应用。

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引用次数: 0

GPF-GAN: An unsupervised generative adversarial network for joint gradient and pixel-constrained fusion of infrared and visible images GPF-GAN：用于红外和可见光图像联合梯度和像素约束融合的无监督生成对抗网络

IF 3 3区工程技术 Q2 ENGINEERING, ELECTRICAL & ELECTRONIC

Digital Signal Processing

Pub Date : 2026-01-09 DOI: 10.1016/j.dsp.2026.105902

Pengpeng Xie, Ziyang Ding, Qianfan Li, Cong Shi, Shibo Bin

Current image fusion algorithms often face modality preference issues: they either excessively depend on the thermal radiation features of infrared images, leading to the loss of visible light texture details, or they prioritize visible light images, which undermines infrared target detection. This makes it challenging to achieve a dynamic balance and collaborative optimization of information from both modalities in complex scenarios. This asymmetric fusion approach makes it difficult for the system to simultaneously preserve sensitivity to thermal radiation targets while maintaining the ability to resolve texture details under extreme lighting conditions. To address this, the paper proposes an infrared and visible light fusion model that incorporates a gradient-pixel joint constraint. Our approach eliminates the complexity and uncertainty associated with manual feature extraction, while effectively leveraging shallow features through multiple shortcut connections. Within the framework of Generative Adversarial Networks, we design a gradient-pixel joint loss function that strikes a balance between preserving significant targets in the infrared image and maintaining the texture structure in the visible light image, thereby enhancing image detail and retaining high-contrast information. To thoroughly evaluate the performance of the proposed method, we conducted systematic experiments using the TNO and RoadScene benchmark datasets, comparing it with eleven state-of-the-art fusion algorithms. The experimental results demonstrate that the proposed method offers significant advantages in both subjective visual quality and objective evaluation metrics. In terms of qualitative evaluation, the fusion results not only preserve natural lighting transitions but, more importantly, accentuate thermal radiation targets in the infrared image while fully retaining the texture details of the visible light image. Quantitative analysis reveals that the proposed method significantly improves metrics such as Mutual Information (MI) and Spatial Frequency (SF). This provides new insights in the field of multimodal image fusion and contributes to balancing the complementary advantages of different modality features.

目前的图像融合算法往往面临模态偏好问题：要么过度依赖红外图像的热辐射特征，导致丢失可见光纹理细节，要么优先考虑可见光图像，从而破坏红外目标检测。这使得在复杂场景中实现两种模式信息的动态平衡和协作优化具有挑战性。这种不对称融合方法使得系统难以同时保持对热辐射目标的敏感性，同时保持在极端光照条件下解决纹理细节的能力。为了解决这一问题，本文提出了一种包含梯度-像素联合约束的红外和可见光融合模型。我们的方法消除了人工特征提取的复杂性和不确定性，同时通过多个快捷连接有效地利用了浅层特征。在生成对抗网络的框架内，我们设计了一个梯度-像素联合损失函数，在保留红外图像中的重要目标和保留可见光图像中的纹理结构之间取得平衡，从而增强图像细节并保留高对比度信息。为了全面评估该方法的性能，我们使用TNO和RoadScene基准数据集进行了系统实验，并将其与11种最先进的融合算法进行了比较。实验结果表明，该方法在主观视觉质量和客观评价指标上都具有显著的优势。在定性评价方面，融合结果不仅保留了自然光照过渡，更重要的是在充分保留可见光图像纹理细节的同时，突出了红外图像中的热辐射目标。定量分析表明，该方法显著提高了互信息（MI）和空间频率（SF）等指标。这为多模态图像融合领域提供了新的见解，有助于平衡不同模态特征的互补优势。

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引用次数: 0