Digital Signal Processing最新文献_第7页

Low-light image enhancement guided by multi-domain features for detail and texture enhancement 利用多域特征增强低照度图像的细节和纹理

IF 2.9 3区工程技术 Q2 ENGINEERING, ELECTRICAL & ELECTRONIC

Digital Signal Processing

Pub Date : 2024-10-03 DOI: 10.1016/j.dsp.2024.104808

Xiaoyang Shen , Haibin Li , Yaqian Li , Wenming Zhang

Low-light image enhancement holds significant value in the fields of computer vision and image processing, such as in applications like surveillance photography and medical imaging. Images captured in low-light environments typically suffer from significant noise levels, low contrast, and color distortion. Although existing low-light image enhancement techniques can improve image brightness and contrast to some extent, they often introduce noise or result in over-enhancement, leading to the loss of detail and texture. This paper introduces an innovative approach to low-light image enhancement by fusing spatial and frequency domain features while optimizing them with multiple loss functions. The core of the algorithm lies in its multi-branch feature extraction, multi-loss function constraints, and a carefully designed model structure. In particular, the model employs an encoder-decoder architecture, where the encoder extracts spatial features from the image, the Fourier feature extraction module captures frequency domain information, and the histogram feature encoder-decoder module processes global brightness distribution. These extracted features are then fused and reconstructed in the decoder to produce the enhanced image. In terms of loss functions, the algorithm combines perceptual loss, structural similarity loss, Fourier loss, and histogram loss to ensure comprehensive and natural enhancement effects. The novelty of this algorithm lies not only in its multi-branch feature extraction design but also in its unique model structure, which synergistically improves image quality across different domains, effectively preventing over-enhancement, and ultimately achieving a balanced enhancement of brightness, details, and texture. Experimental results on multiple datasets, including SIDD, LOL, MIT-Adobe-FiveK, and LOL-v2-synthetic, demonstrate that the proposed method outperforms existing techniques in terms of image detail, texture, and brightness. Specifically, it achieves a PSNR of 27.52 dB on the LOL dataset, surpassing Wavelet Diffusion by 1.19 dB. Additionally, on the LOL-v2-synthetic dataset, it achieves a PSNR of 29.56 dB, exceeding Wavelet Diffusion by 3.06 dB. These results demonstrate a significant enhancement in the visual quality of low-light images.

弱光图像增强技术在计算机视觉和图像处理领域具有重要价值，例如在监控摄影和医疗成像等应用中。在低照度环境下拍摄的图像通常会出现明显的噪点、低对比度和色彩失真。虽然现有的低照度图像增强技术能在一定程度上提高图像亮度和对比度，但它们往往会引入噪点或导致过度增强，从而导致细节和纹理的丢失。本文介绍了一种创新的低照度图像增强方法，它融合了空间域和频域特征，同时利用多种损失函数对其进行优化。该算法的核心在于其多分支特征提取、多损失函数约束和精心设计的模型结构。特别是，该模型采用了编码器-解码器架构，其中编码器从图像中提取空间特征，傅立叶特征提取模块捕捉频域信息，直方图特征编码器-解码器模块处理全局亮度分布。然后在解码器中融合和重建这些提取的特征，生成增强图像。在损失函数方面，该算法结合了感知损失、结构相似性损失、傅里叶损失和直方图损失，以确保全面而自然的增强效果。该算法的新颖之处不仅在于其多分支特征提取设计，还在于其独特的模型结构，能协同改善不同领域的图像质量，有效防止过度增强，最终实现亮度、细节和纹理的均衡增强。在 SIDD、LOL、MIT-Adobe-FiveK 和 LOL-v2-synthetic 等多个数据集上的实验结果表明，所提出的方法在图像细节、纹理和亮度方面都优于现有技术。具体来说，它在 LOL 数据集上的 PSNR 达到 27.52 dB，比小波扩散技术高出 1.19 dB。此外，在 LOL-v2 合成数据集上，它的 PSNR 达到 29.56 dB，比小波扩散高出 3.06 dB。这些结果表明，小波扩散技术显著提高了低照度图像的视觉质量。

{"title":"Low-light image enhancement guided by multi-domain features for detail and texture enhancement","authors":"Xiaoyang Shen , Haibin Li , Yaqian Li , Wenming Zhang","doi":"10.1016/j.dsp.2024.104808","DOIUrl":"10.1016/j.dsp.2024.104808","url":null,"abstract":"<div><div>Low-light image enhancement holds significant value in the fields of computer vision and image processing, such as in applications like surveillance photography and medical imaging. Images captured in low-light environments typically suffer from significant noise levels, low contrast, and color distortion. Although existing low-light image enhancement techniques can improve image brightness and contrast to some extent, they often introduce noise or result in over-enhancement, leading to the loss of detail and texture. This paper introduces an innovative approach to low-light image enhancement by fusing spatial and frequency domain features while optimizing them with multiple loss functions. The core of the algorithm lies in its multi-branch feature extraction, multi-loss function constraints, and a carefully designed model structure. In particular, the model employs an encoder-decoder architecture, where the encoder extracts spatial features from the image, the Fourier feature extraction module captures frequency domain information, and the histogram feature encoder-decoder module processes global brightness distribution. These extracted features are then fused and reconstructed in the decoder to produce the enhanced image. In terms of loss functions, the algorithm combines perceptual loss, structural similarity loss, Fourier loss, and histogram loss to ensure comprehensive and natural enhancement effects. The novelty of this algorithm lies not only in its multi-branch feature extraction design but also in its unique model structure, which synergistically improves image quality across different domains, effectively preventing over-enhancement, and ultimately achieving a balanced enhancement of brightness, details, and texture. Experimental results on multiple datasets, including SIDD, LOL, MIT-Adobe-FiveK, and LOL-v2-synthetic, demonstrate that the proposed method outperforms existing techniques in terms of image detail, texture, and brightness. Specifically, it achieves a PSNR of 27.52 dB on the LOL dataset, surpassing Wavelet Diffusion by 1.19 dB. Additionally, on the LOL-v2-synthetic dataset, it achieves a PSNR of 29.56 dB, exceeding Wavelet Diffusion by 3.06 dB. These results demonstrate a significant enhancement in the visual quality of low-light images.</div></div>","PeriodicalId":51011,"journal":{"name":"Digital Signal Processing","volume":"156 ","pages":"Article 104808"},"PeriodicalIF":2.9,"publicationDate":"2024-10-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142428063","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Distributed multi-sensor multi-target tracking with fault detection and exclusion using belief propagation 利用信念传播进行故障检测和排除的分布式多传感器多目标跟踪

IF 2.9 3区工程技术 Q2 ENGINEERING, ELECTRICAL & ELECTRONIC

Digital Signal Processing

Pub Date : 2024-10-03 DOI: 10.1016/j.dsp.2024.104797

Yanbo Xue, Yunfei Guo, Dongsheng Yang, Hao Zhang, Han Shen-tu

Multi-sensor multi-target tracking (MMT) is widely used in civilian and military fields. However, as the number of sensor nodes increases, so does the probability of the sensor node faults corrupting the system. In order to guarantee the tracking performance in the presence of faulty sensors, a distributed MMT algorithm in clutter with sensor fault detection and exclusion under the belief propagation framework (FDE-BP) is proposed in this paper. Firstly, a novel FDE method using the fused residual is proposed to detect the faulty sensors in clutter. To ensure the independence among the fused residuals of different targets, a measurement partition method based on the assignment matrix is proposed. The partition of measurements makes the factor graph have a tree structure rather than a loop one, which reduces the computational complexity. Secondly, the MMT problem is presented by a factor graph model to fuse the information among distributed sensor nodes, and a Gaussian version of FDE-BP is derived. The simulation results show that the proposed FDE-BP algorithm can guarantee the tracking performance in the presence of different types of sensor faults.

多传感器多目标跟踪（MMT）被广泛应用于民用和军用领域。然而，随着传感器节点数量的增加，传感器节点故障破坏系统的概率也在增加。为了保证故障传感器存在时的跟踪性能，本文提出了一种杂波中的分布式 MMT 算法，并在信念传播框架下进行传感器故障检测和排除（FDE-BP）。首先，本文提出了一种使用融合残差的新型 FDE 方法来检测杂波中的故障传感器。为了确保不同目标的融合残差之间的独立性，本文提出了一种基于赋值矩阵的测量分区方法。测量分区使因子图成为树状结构而非环状结构，从而降低了计算复杂度。其次，通过因子图模型提出了 MMT 问题，以融合分布式传感器节点之间的信息，并推导出高斯版本的 FDE-BP。仿真结果表明，所提出的 FDE-BP 算法能在不同类型的传感器故障情况下保证跟踪性能。

{"title":"Distributed multi-sensor multi-target tracking with fault detection and exclusion using belief propagation","authors":"Yanbo Xue, Yunfei Guo, Dongsheng Yang, Hao Zhang, Han Shen-tu","doi":"10.1016/j.dsp.2024.104797","DOIUrl":"10.1016/j.dsp.2024.104797","url":null,"abstract":"<div><div>Multi-sensor multi-target tracking (MMT) is widely used in civilian and military fields. However, as the number of sensor nodes increases, so does the probability of the sensor node faults corrupting the system. In order to guarantee the tracking performance in the presence of faulty sensors, a distributed MMT algorithm in clutter with sensor fault detection and exclusion under the belief propagation framework (FDE-BP) is proposed in this paper. Firstly, a novel FDE method using the fused residual is proposed to detect the faulty sensors in clutter. To ensure the independence among the fused residuals of different targets, a measurement partition method based on the assignment matrix is proposed. The partition of measurements makes the factor graph have a tree structure rather than a loop one, which reduces the computational complexity. Secondly, the MMT problem is presented by a factor graph model to fuse the information among distributed sensor nodes, and a Gaussian version of FDE-BP is derived. The simulation results show that the proposed FDE-BP algorithm can guarantee the tracking performance in the presence of different types of sensor faults.</div></div>","PeriodicalId":51011,"journal":{"name":"Digital Signal Processing","volume":"156 ","pages":"Article 104797"},"PeriodicalIF":2.9,"publicationDate":"2024-10-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142428064","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Addressing preprocessing for spectrum sensing using image processing 利用图像处理解决频谱传感的预处理问题

IF 2.9 3区工程技术 Q2 ENGINEERING, ELECTRICAL & ELECTRONIC

Digital Signal Processing

Pub Date : 2024-10-03 DOI: 10.1016/j.dsp.2024.104800

Andres Rojas , Gordana Jovanovic Dolecek , José M. de la Rosa

This paper presents a novel approach to preprocessing spectrograms for spectrum sensing (SS) from the image-processing perspective. Gaussian bilateral filtering, a common image-denoising technique, has been introduced to improve spectrograms in SS in high-noise environments. This approach is evaluated by simulating LTE and 5 G NR signal spectrograms across various signal-to-noise ratios (SNRs). An extensive review and comparison with recent spectrogram-based works for different applications demonstrated that the proposed approach does not depend on deep learning models to denoise spectrograms, showing a simpler yet effective strategy to address SS.

本文从图像处理的角度提出了一种新颖的光谱传感（SS）谱图预处理方法。本文引入了高斯双边滤波这一常见的图像去噪技术，以改进高噪声环境下频谱传感中的频谱图。通过模拟各种信噪比（SNR）下的 LTE 和 5 G NR 信号频谱图，对这种方法进行了评估。通过对不同应用中基于频谱图的最新研究成果进行广泛的回顾和比较，证明所提出的方法并不依赖于深度学习模型来对频谱图进行去噪，从而为解决 SS 问题提供了一种更简单而有效的策略。

引用次数: 0

Semi-supervised sound event detection with dynamic convolution and confidence-aware mean teacher 利用动态卷积和置信度感知平均值教师进行半监督声音事件检测

IF 2.9 3区工程技术 Q2 ENGINEERING, ELECTRICAL & ELECTRONIC

Digital Signal Processing

Pub Date : 2024-10-02 DOI: 10.1016/j.dsp.2024.104794

Shengchang Xiao , Xueshuai Zhang , Pengyuan Zhang , Yonghong Yan

Recently, sound event detection (SED) has made significant advancements through the application of deep learning, but there are still many difficulties and challenges to be addressed. One of the major challenges is the diversity of sound events, leading to substantial variations in time-frequency domain features. Additionally, most existing SED models can not effectively handle sound events of different scales, particularly those of short duration. Another challenge is the lack of well labeled dataset. The commonly used solution is mean teacher method, but inaccurate pseudo-labels could lead to confirmation bias and performance imbalance. In this paper, we introduce the multi-dimensional frequency dynamic convolution, which endows convolutional kernels with frequency-adaptive dynamic properties to enhance the feature representation capability. Moreover, we propose dual self attention pooling function to achieve more precise temporal localization. Finally, to solve the incorrect pseudo-labels problems, we propose the confidence-aware mean teacher to increase pseudo-labels accuracy and train the student model with high confidence labels. Experimental results on DCASE2017, DCASE2018 and DCASE2023 Task4 dataset validate the superior performance of proposed methods.

最近，通过深度学习的应用，声音事件检测（SED）取得了重大进展，但仍有许多困难和挑战有待解决。其中一个主要挑战是声音事件的多样性，这导致了时频域特征的巨大变化。此外，大多数现有的 SED 模型无法有效处理不同规模的声音事件，尤其是持续时间较短的声音事件。另一个挑战是缺乏标注良好的数据集。常用的解决方案是平均教师法，但不准确的伪标签可能会导致确认偏差和性能失衡。在本文中，我们引入了多维频率动态卷积，赋予卷积核以频率自适应动态特性，以增强特征表示能力。此外，我们还提出了双自注意力池函数，以实现更精确的时间定位。最后，为了解决伪标签不正确的问题，我们提出了置信度感知平均教师来提高伪标签的准确性，并用高置信度标签训练学生模型。在 DCASE2017、DCASE2018 和 DCASE2023 Task4 数据集上的实验结果验证了所提方法的优越性能。

{"title":"Semi-supervised sound event detection with dynamic convolution and confidence-aware mean teacher","authors":"Shengchang Xiao , Xueshuai Zhang , Pengyuan Zhang , Yonghong Yan","doi":"10.1016/j.dsp.2024.104794","DOIUrl":"10.1016/j.dsp.2024.104794","url":null,"abstract":"<div><div>Recently, sound event detection (SED) has made significant advancements through the application of deep learning, but there are still many difficulties and challenges to be addressed. One of the major challenges is the diversity of sound events, leading to substantial variations in time-frequency domain features. Additionally, most existing SED models can not effectively handle sound events of different scales, particularly those of short duration. Another challenge is the lack of well labeled dataset. The commonly used solution is mean teacher method, but inaccurate pseudo-labels could lead to confirmation bias and performance imbalance. In this paper, we introduce the multi-dimensional frequency dynamic convolution, which endows convolutional kernels with frequency-adaptive dynamic properties to enhance the feature representation capability. Moreover, we propose dual self attention pooling function to achieve more precise temporal localization. Finally, to solve the incorrect pseudo-labels problems, we propose the confidence-aware mean teacher to increase pseudo-labels accuracy and train the student model with high confidence labels. Experimental results on DCASE2017, DCASE2018 and DCASE2023 Task4 dataset validate the superior performance of proposed methods.</div></div>","PeriodicalId":51011,"journal":{"name":"Digital Signal Processing","volume":"156 ","pages":"Article 104794"},"PeriodicalIF":2.9,"publicationDate":"2024-10-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142427650","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

On the skew-symmetric binary sequences and the merit factor problem 关于倾斜对称二进制序列和优点因子问题

IF 2.9 3区工程技术 Q2 ENGINEERING, ELECTRICAL & ELECTRONIC

Digital Signal Processing

Pub Date : 2024-10-02 DOI: 10.1016/j.dsp.2024.104793

Miroslav Dimitrov

The merit factor problem is of practical importance to manifold domains, such as digital communications engineering, radars, system modulation, system testing, information theory, physics, chemistry. In this work, some useful mathematical properties related to the flip operation of the skew-symmetric binary sequences are presented. By exploiting those properties, the space complexity of state-of-the-art stochastic merit factor optimization algorithms could be reduced from

O (n^{2})

to

O (n)

. As a proof of concept, a lightweight stochastic algorithm was constructed, which can optimize pseudo-randomly generated skew-symmetric binary sequences with long lengths (up to

10^{5} + 1

) to skew-symmetric binary sequences with a merit factor greater than 5. An approximation of the required time is also provided. The numerical experiments suggest that the algorithm is universal and could be applied to skew-symmetric binary sequences with arbitrary lengths.

绩因问题对数字通信工程、雷达、系统调制、系统测试、信息论、物理学、化学等多个领域都具有重要的实际意义。本研究提出了一些与偏斜对称二进制序列的翻转操作相关的有用数学特性。利用这些特性，最先进的随机优点因子优化算法的空间复杂度可从 O(n2) 降至 O(n)。作为概念验证，我们构建了一种轻量级随机算法，它可以将伪随机生成的长度较长（达 105+1）的偏斜对称二进制序列优化为优点因子大于 5 的偏斜对称二进制序列。同时还提供了所需时间的近似值。数值实验表明，该算法具有通用性，可用于任意长度的偏斜对称二进制序列。

{"title":"On the skew-symmetric binary sequences and the merit factor problem","authors":"Miroslav Dimitrov","doi":"10.1016/j.dsp.2024.104793","DOIUrl":"10.1016/j.dsp.2024.104793","url":null,"abstract":"<div><div>The merit factor problem is of practical importance to manifold domains, such as digital communications engineering, radars, system modulation, system testing, information theory, physics, chemistry. In this work, some useful mathematical properties related to the flip operation of the skew-symmetric binary sequences are presented. By exploiting those properties, the space complexity of state-of-the-art stochastic merit factor optimization algorithms could be reduced from <span><math><mi>O</mi><mo>(</mo><msup><mrow><mi>n</mi></mrow><mrow><mn>2</mn></mrow></msup><mo>)</mo></math></span> to <span><math><mi>O</mi><mo>(</mo><mi>n</mi><mo>)</mo></math></span>. As a proof of concept, a lightweight stochastic algorithm was constructed, which can optimize pseudo-randomly generated skew-symmetric binary sequences with long lengths (up to <span><math><msup><mrow><mn>10</mn></mrow><mrow><mn>5</mn></mrow></msup><mo>+</mo><mn>1</mn></math></span>) to skew-symmetric binary sequences with a merit factor greater than 5. An approximation of the required time is also provided. The numerical experiments suggest that the algorithm is universal and could be applied to skew-symmetric binary sequences with arbitrary lengths.</div></div>","PeriodicalId":51011,"journal":{"name":"Digital Signal Processing","volume":"156 ","pages":"Article 104793"},"PeriodicalIF":2.9,"publicationDate":"2024-10-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142428115","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Secure distributed estimation via an average diffusion LMS and average likelihood ratio test 通过平均扩散 LMS 和平均似然比检验进行安全分布式估计

IF 2.9 3区工程技术 Q2 ENGINEERING, ELECTRICAL & ELECTRONIC

Digital Signal Processing

Pub Date : 2024-10-01 DOI: 10.1016/j.dsp.2024.104782

Hadi Zayyani , Mehdi Korki

Secure distributed estimation algorithms are designed to protect against a spectrum of attacks by exploring different attack models and implementing strategies to enhance the resilience of the algorithm. These models encompass diverse scenarios such as measurement sensor attacks and communication link attacks, which have been extensively investigated in existing literature. This paper, however, focuses on a specific type of attack: the multiplicative sensor attack model. To counter this, the paper introduces the Average diffusion least mean square (ADLMS) algorithm as a viable solution. Furthermore, the paper introduces the Average Likelihood Ratio Test (ALRT) detector, which provides a straightforward detection criterion. In the presence of communication link attacks, the paper considers the manipulation attack model and presents an ALRT adversary detector. The analysis extends to these ALRT detectors, encompassing the calculation of adversary detection probability and false alarm probability, both achieved in closed form. The paper also provides the mean convergence analysis of the proposed ADLMS algorithm. Simulation results reveal that the proposed algorithms exhibit enhanced performance compared to the DLMS algorithm, while the incremental complexity remains only marginally higher than that of the DLMS algorithm.

安全分布式估算算法旨在通过探索不同的攻击模型和实施增强算法弹性的策略来抵御各种攻击。这些模型包括多种情况，如测量传感器攻击和通信链路攻击，现有文献已对这些情况进行了广泛研究。不过，本文重点关注一种特定类型的攻击：乘法传感器攻击模型。为了应对这种攻击，本文引入了平均扩散最小均方算法（ADLMS）作为可行的解决方案。此外，本文还介绍了平均似然比检验（ALRT）检测器，它提供了一种直接的检测标准。在存在通信链路攻击的情况下，本文考虑了操纵攻击模型，并提出了 ALRT 对手检测器。分析扩展到这些 ALRT 检测器，包括对手检测概率和误报概率的计算，两者都以封闭形式实现。论文还对所提出的 ADLMS 算法进行了平均收敛分析。仿真结果表明，与 DLMS 算法相比，所提出的算法表现出更强的性能，而增量复杂度仍然只略高于 DLMS 算法。

{"title":"Secure distributed estimation via an average diffusion LMS and average likelihood ratio test","authors":"Hadi Zayyani , Mehdi Korki","doi":"10.1016/j.dsp.2024.104782","DOIUrl":"10.1016/j.dsp.2024.104782","url":null,"abstract":"<div><div>Secure distributed estimation algorithms are designed to protect against a spectrum of attacks by exploring different attack models and implementing strategies to enhance the resilience of the algorithm. These models encompass diverse scenarios such as measurement sensor attacks and communication link attacks, which have been extensively investigated in existing literature. This paper, however, focuses on a specific type of attack: the multiplicative sensor attack model. To counter this, the paper introduces the Average diffusion least mean square (ADLMS) algorithm as a viable solution. Furthermore, the paper introduces the Average Likelihood Ratio Test (ALRT) detector, which provides a straightforward detection criterion. In the presence of communication link attacks, the paper considers the manipulation attack model and presents an ALRT adversary detector. The analysis extends to these ALRT detectors, encompassing the calculation of adversary detection probability and false alarm probability, both achieved in closed form. The paper also provides the mean convergence analysis of the proposed ADLMS algorithm. Simulation results reveal that the proposed algorithms exhibit enhanced performance compared to the DLMS algorithm, while the incremental complexity remains only marginally higher than that of the DLMS algorithm.</div></div>","PeriodicalId":51011,"journal":{"name":"Digital Signal Processing","volume":"156 ","pages":"Article 104782"},"PeriodicalIF":2.9,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142428113","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Non-coherent short-packet communications: Novel z-domain user multiplexing 非相干短包通信：新颖的 z 域用户多路复用

IF 2.9 3区工程技术 Q2 ENGINEERING, ELECTRICAL & ELECTRONIC

Digital Signal Processing

Pub Date : 2024-10-01 DOI: 10.1016/j.dsp.2024.104777

Tuncay Eren

In the evolution of fifth generation (5G) and beyond wireless communication systems, non-coherent (NC) short packet communication (SPC) is crucial for achieving ultra-reliable low-latency communication (URLLC). Frame design, latency, and reliability are some of the challenges associated with short-packet communication. Recently, to address these challenges, a novel modulation scheme known as modulation on conjugate-reciprocal zeros (MOCZ) has been proposed. MOCZ modulates information on conjugate reciprocal zeros in the z-domain, thereby eliminating the need for channel estimation and providing a robust solution for NC communication. However, in multi-user MOCZ (MU-MOCZ) scheme, adding guard intervals to each short packet to mitigate channel impact remains an issue, as it increases the transmission time and consequently reduces efficiency. To address the aforementioned problem, this paper introduces a novel frame design approach called z-domain user multiplexing MOCZ (ZDUM-MOCZ or ZDM-MOCZ). Unlike traditional time division multiplexing (TDM), which serves users consecutively in the time domain, this method multiplexes users in the z-domain. In this approach, each user is allocated a specific set of zeros in the z-domain, which collectively form a unique sequence in the time domain. The findings illustrate the potential for reduced latency in downlink transmission, highlighting the benefits of this novel methodology over conventional MU-MOCZ method. The proposed ZDUM-MOCZ scheme not only addresses the existing issues in the frame design of the MU-MOCZ scheme but also facilitates more efficient and reliable short packet communication in 5G and beyond wireless systems.

在第五代（5G）及以后的无线通信系统中，非相干（NC）短数据包通信（SPC）对于实现超可靠低延迟通信（URLLC）至关重要。帧设计、延迟和可靠性是与短数据包通信相关的一些挑战。最近，为了应对这些挑战，有人提出了一种新的调制方案，即共轭倒数零点调制（MOCZ）。MOCZ 将信息调制在 z 域的共轭倒数零点上，因此无需进行信道估计，为数控通信提供了一种稳健的解决方案。然而，在多用户 MOCZ（MU-MOCZ）方案中，为每个短数据包添加保护间隔以减轻信道影响仍是一个问题，因为这会增加传输时间，从而降低效率。为解决上述问题，本文提出了一种新颖的帧设计方法，称为 z 域用户复用 MOCZ（ZDUM-MOCZ 或 ZDM-MOCZ）。与在时域连续服务用户的传统时分复用（TDM）不同，这种方法在 z 域复用用户。在这种方法中，每个用户在 z 域被分配一组特定的零，这些零在时域中共同形成一个独特的序列。研究结果表明，ZDUM-MOCZ 有可能缩短下行链路传输的延迟时间，凸显了这种新方法与传统 MU-MOCZ 方法相比的优势。所提出的 ZDUM-MOCZ 方案不仅解决了 MU-MOCZ 方案帧设计中的现有问题，还有助于在 5G 及其他无线系统中实现更高效、更可靠的短数据包通信。

{"title":"Non-coherent short-packet communications: Novel z-domain user multiplexing","authors":"Tuncay Eren","doi":"10.1016/j.dsp.2024.104777","DOIUrl":"10.1016/j.dsp.2024.104777","url":null,"abstract":"<div><div>In the evolution of fifth generation (5G) and beyond wireless communication systems, non-coherent (NC) short packet communication (SPC) is crucial for achieving ultra-reliable low-latency communication (URLLC). Frame design, latency, and reliability are some of the challenges associated with short-packet communication. Recently, to address these challenges, a novel modulation scheme known as modulation on conjugate-reciprocal zeros (MOCZ) has been proposed. MOCZ modulates information on conjugate reciprocal zeros in the z-domain, thereby eliminating the need for channel estimation and providing a robust solution for NC communication. However, in multi-user MOCZ (MU-MOCZ) scheme, adding guard intervals to each short packet to mitigate channel impact remains an issue, as it increases the transmission time and consequently reduces efficiency. To address the aforementioned problem, this paper introduces a novel frame design approach called z-domain user multiplexing MOCZ (ZDUM-MOCZ or ZDM-MOCZ). Unlike traditional time division multiplexing (TDM), which serves users consecutively in the time domain, this method multiplexes users in the z-domain. In this approach, each user is allocated a specific set of zeros in the z-domain, which collectively form a unique sequence in the time domain. The findings illustrate the potential for reduced latency in downlink transmission, highlighting the benefits of this novel methodology over conventional MU-MOCZ method. The proposed ZDUM-MOCZ scheme not only addresses the existing issues in the frame design of the MU-MOCZ scheme but also facilitates more efficient and reliable short packet communication in 5G and beyond wireless systems.</div></div>","PeriodicalId":51011,"journal":{"name":"Digital Signal Processing","volume":"156 ","pages":"Article 104777"},"PeriodicalIF":2.9,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142427649","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

EFRNet: Edge feature refinement network for real-time semantic segmentation of driving scenes EFRNet：用于驾驶场景实时语义分割的边缘特征细化网络

IF 2.9 3区工程技术 Q2 ENGINEERING, ELECTRICAL & ELECTRONIC

Digital Signal Processing

Pub Date : 2024-10-01 DOI: 10.1016/j.dsp.2024.104791

Zhiqiang Hou , Minjie Qu , Minjie Cheng , Sugang Ma , Yunchen Wang , Xiaobao Yang

In the semantic segmentation field, the dual-branch structure is a highly effective segmentation model. However, the frequent downsampling in the semantic branch reduces the accuracy of features expression with increasing network depth, resulting in suboptimal segmentation performance. To address the above issues, this paper proposes a real-time semantic segmentation network based on Edge Feature Refinement (Edge Feature Refinement Network, EFRNet). A dual-branch structure is used in the encoder. To enhance the accuracy of deep features expression in the network, an edge refinement module (ERM) is designed in the dual-branch interaction stage to refine the features of the two branches and improve segmentation accuracy. In the decoder, a Bilateral Channel Attention (BCA) module is designed, which is used to extract detailed information and semantic information of features at different levels of the network, and gradually restore small target features. To capture multi-scale context information, we introduce a Multi-scale Context Aggregation Module (MCAM), which efficiently integrates multi-scale information in a parallel manner. The proposed algorithm has experimented on Cityscapes and CamVid datasets, and reaches 78.8% mIoU and 79.6% mIoU, with speeds of 81FPS and 115FPS, respectively. Experimental results show that the proposed algorithm effectively improves segmentation performance while maintaining a high segmentation speed.

在语义分割领域，双分支结构是一种高效的分割模型。然而，随着网络深度的增加，语义分支中频繁的下采样降低了特征表达的准确性，导致分割性能不理想。针对上述问题，本文提出了一种基于边缘特征细化的实时语义分割网络（边缘特征细化网络，EFRNet）。编码器采用双分支结构。为了提高网络中深层特征表达的准确性，在双分支交互阶段设计了边缘细化模块（ERM），以细化两个分支的特征，提高分割准确性。在解码器中，我们设计了双通道注意（BCA）模块，用于提取网络中不同层次特征的细节信息和语义信息，并逐步还原小目标特征。为了捕捉多尺度上下文信息，我们引入了多尺度上下文聚合模块（MCAM），以并行的方式有效地整合多尺度信息。所提出的算法在 Cityscapes 和 CamVid 数据集上进行了实验，分别达到了 78.8% mIoU 和 79.6% mIoU，速度分别为 81FPS 和 115FPS。实验结果表明，所提出的算法在保持较高分割速度的同时，有效地提高了分割性能。

{"title":"EFRNet: Edge feature refinement network for real-time semantic segmentation of driving scenes","authors":"Zhiqiang Hou , Minjie Qu , Minjie Cheng , Sugang Ma , Yunchen Wang , Xiaobao Yang","doi":"10.1016/j.dsp.2024.104791","DOIUrl":"10.1016/j.dsp.2024.104791","url":null,"abstract":"<div><div>In the semantic segmentation field, the dual-branch structure is a highly effective segmentation model. However, the frequent downsampling in the semantic branch reduces the accuracy of features expression with increasing network depth, resulting in suboptimal segmentation performance. To address the above issues, this paper proposes a real-time semantic segmentation network based on Edge Feature Refinement (Edge Feature Refinement Network, EFRNet). A dual-branch structure is used in the encoder. To enhance the accuracy of deep features expression in the network, an edge refinement module (ERM) is designed in the dual-branch interaction stage to refine the features of the two branches and improve segmentation accuracy. In the decoder, a Bilateral Channel Attention (BCA) module is designed, which is used to extract detailed information and semantic information of features at different levels of the network, and gradually restore small target features. To capture multi-scale context information, we introduce a Multi-scale Context Aggregation Module (MCAM), which efficiently integrates multi-scale information in a parallel manner. The proposed algorithm has experimented on Cityscapes and CamVid datasets, and reaches 78.8% mIoU and 79.6% mIoU, with speeds of 81FPS and 115FPS, respectively. Experimental results show that the proposed algorithm effectively improves segmentation performance while maintaining a high segmentation speed.</div></div>","PeriodicalId":51011,"journal":{"name":"Digital Signal Processing","volume":"156 ","pages":"Article 104791"},"PeriodicalIF":2.9,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142427690","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Towards generalized face forgery detection with domain-robust representation learning 利用领域可靠的表征学习实现通用人脸伪造检测

IF 2.9 3区工程技术 Q2 ENGINEERING, ELECTRICAL & ELECTRONIC

Digital Signal Processing

Pub Date : 2024-09-30 DOI: 10.1016/j.dsp.2024.104792

Caiyu Li, Yan Wo

Face forgery detection is crucial for the security of digital identities. However, existing methods often struggle to generalize effectively to unseen domains due to the domain shift between training and testing data. We propose a Domain-robust Representation Learning (DRRL) method for generalized face forgery detection. Specifically, we observe that domain shifts in face forgery detection tasks are often caused by forgery differences and content differences between domain data, while the limitations of training data lead the model to overfit to these feature expressions in the seen domain. Therefore, DRRL enhances the model's generalization to unseen domains by first adding representative data representations to mitigate overfitting to seen data and then removing the features of expressed domain information to learn a robust, discriminative representation of domain variation. Data augmentation is achieved by stylizing sample representations and exploring representative new styles to generate rich data variants, with the Content-style Augmentation (CSA) module and Forgery-style Augmentation (FSA) module implemented for content and forgery expression, respectively. Based on this, the Content Decorrelation (CTD) module and Sensitive Channels Drop (SCD) module are used to remove content features irrelevant to forgery and domain-sensitive forgery features, encouraging the model to focus on clean and robust forgery features, thereby achieving the goal of learning domain-robust representations. Extensive experiments on five large-scale datasets demonstrate that our method exhibits advanced and stable generalization performance in practical scenarios.

人脸伪造检测对数字身份安全至关重要。然而，由于训练数据和测试数据之间的领域转移，现有方法往往难以有效地推广到未知领域。我们提出了一种用于通用人脸伪造检测的领域稳健表征学习（DRRL）方法。具体来说，我们观察到人脸伪造检测任务中的域转移通常是由域数据之间的伪造差异和内容差异引起的，而训练数据的局限性导致模型在可见域中过度拟合这些特征表达。因此，DRRL 首先通过添加有代表性的数据表示来减轻对所见数据的过度拟合，然后移除所表达的领域信息特征，从而学习领域变化的稳健性和鉴别性表征，从而增强模型对未见领域的泛化能力。数据增强是通过将样本表示风格化和探索有代表性的新风格来生成丰富的数据变体来实现的，内容风格增强（CSA）模块和伪造风格增强（FSA）模块分别用于内容和伪造表达。在此基础上，利用内容去相关性（Content Decorrelation，CTD）模块和敏感通道去除（Sensitive Channels Drop，SCD）模块去除与伪造无关的内容特征和对领域敏感的伪造特征，促使模型专注于干净、稳健的伪造特征，从而实现学习领域稳健表征的目标。在五个大规模数据集上进行的广泛实验证明，我们的方法在实际应用场景中表现出先进而稳定的泛化性能。

{"title":"Towards generalized face forgery detection with domain-robust representation learning","authors":"Caiyu Li, Yan Wo","doi":"10.1016/j.dsp.2024.104792","DOIUrl":"10.1016/j.dsp.2024.104792","url":null,"abstract":"<div><div>Face forgery detection is crucial for the security of digital identities. However, existing methods often struggle to generalize effectively to unseen domains due to the domain shift between training and testing data. We propose a Domain-robust Representation Learning (DRRL) method for generalized face forgery detection. Specifically, we observe that domain shifts in face forgery detection tasks are often caused by forgery differences and content differences between domain data, while the limitations of training data lead the model to overfit to these feature expressions in the seen domain. Therefore, DRRL enhances the model's generalization to unseen domains by first adding representative data representations to mitigate overfitting to seen data and then removing the features of expressed domain information to learn a robust, discriminative representation of domain variation. Data augmentation is achieved by stylizing sample representations and exploring representative new styles to generate rich data variants, with the Content-style Augmentation (CSA) module and Forgery-style Augmentation (FSA) module implemented for content and forgery expression, respectively. Based on this, the Content Decorrelation (CTD) module and Sensitive Channels Drop (SCD) module are used to remove content features irrelevant to forgery and domain-sensitive forgery features, encouraging the model to focus on clean and robust forgery features, thereby achieving the goal of learning domain-robust representations. Extensive experiments on five large-scale datasets demonstrate that our method exhibits advanced and stable generalization performance in practical scenarios.</div></div>","PeriodicalId":51011,"journal":{"name":"Digital Signal Processing","volume":"156 ","pages":"Article 104792"},"PeriodicalIF":2.9,"publicationDate":"2024-09-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142428118","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Automatic thresholding method using single information entropy under product transformation of order difference filter response 阶差滤波器响应乘积变换下的单信息熵自动阈值法

IF 2.9 3区工程技术 Q2 ENGINEERING, ELECTRICAL & ELECTRONIC

Digital Signal Processing

Pub Date : 2024-09-30 DOI: 10.1016/j.dsp.2024.104798

Yaobin Zou , Shutong Chen

To automatically threshold images with unimodal, bimodal, multimodal or non-modal gray level distributions within a unified framework, an automatic thresholding method using single information entropy under the product transformation of order difference filter response is proposed. The proposed method first performs the product transformation of order difference filter response on an input image at different scales to obtain the product transformation image. Critical or non-critical pixels are labelled on each pixel of the binary images corresponding to different thresholds to construct a series of binary label images that are used for distinguishing critical or non-critical regions. A single information entropy is finally used for characterizing the information obtained from the product transformation image with the critical regions of different binary label images, and the threshold corresponding to maximum information entropy is selected as final threshold. The proposed method is compared with seven state-of-the-art segmentation methods. Experimental results on 12 synthetic images and 98 real-world images show that the average Matthews correlation coefficients of the proposed method reached 0.994 and 0.966 for the synthetic images and the real-world images, which outperform the second-best method by 52.4 % and 27.8 %, respectively. The proposed method has more robust segmentation adaptability to test images with different modalities, despite not offering an advantage in terms of computational efficiency.

为了在统一的框架内自动阈值化具有单模态、双模态、多模态或非模态灰度分布的图像，提出了一种在阶差滤波器响应的乘积变换下使用单信息熵的自动阈值化方法。该方法首先对不同尺度的输入图像进行阶差滤波响应的乘积变换，得到乘积变换图像。在二值图像的每个像素上标注与不同阈值相对应的临界或非临界像素，从而构建一系列二值标签图像，用于区分临界或非临界区域。最后使用单一信息熵来表征产品变换图像与不同二进制标签图像的临界区域所获得的信息，并选择与最大信息熵相对应的阈值作为最终阈值。将所提出的方法与七种最先进的分割方法进行了比较。在 12 幅合成图像和 98 幅真实世界图像上的实验结果表明，所提方法在合成图像和真实世界图像上的平均马修斯相关系数分别达到了 0.994 和 0.966，比排名第二的方法分别高出 52.4% 和 27.8%。尽管在计算效率方面没有优势，但提出的方法对不同模式的测试图像具有更强的分割适应性。

{"title":"Automatic thresholding method using single information entropy under product transformation of order difference filter response","authors":"Yaobin Zou , Shutong Chen","doi":"10.1016/j.dsp.2024.104798","DOIUrl":"10.1016/j.dsp.2024.104798","url":null,"abstract":"<div><div>To automatically threshold images with unimodal, bimodal, multimodal or non-modal gray level distributions within a unified framework, an automatic thresholding method using single information entropy under the product transformation of order difference filter response is proposed. The proposed method first performs the product transformation of order difference filter response on an input image at different scales to obtain the product transformation image. Critical or non-critical pixels are labelled on each pixel of the binary images corresponding to different thresholds to construct a series of binary label images that are used for distinguishing critical or non-critical regions. A single information entropy is finally used for characterizing the information obtained from the product transformation image with the critical regions of different binary label images, and the threshold corresponding to maximum information entropy is selected as final threshold. The proposed method is compared with seven state-of-the-art segmentation methods. Experimental results on 12 synthetic images and 98 real-world images show that the average Matthews correlation coefficients of the proposed method reached 0.994 and 0.966 for the synthetic images and the real-world images, which outperform the second-best method by 52.4 % and 27.8 %, respectively. The proposed method has more robust segmentation adaptability to test images with different modalities, despite not offering an advantage in terms of computational efficiency.</div></div>","PeriodicalId":51011,"journal":{"name":"Digital Signal Processing","volume":"156 ","pages":"Article 104798"},"PeriodicalIF":2.9,"publicationDate":"2024-09-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142427694","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0