Digital Signal Processing最新文献_第7页

Dual-step optimization for binary sequences with high merit factors 高品质因子二值序列的双步优化

IF 2.9 3区工程技术

Digital Signal Processing Pub Date : 2025-05-13 DOI: 10.1016/j.dsp.2025.105316

Blaž Pšeničnik, Rene Mlinarič, Janez Brest, Borko Bošković

{"title":"Dual-step optimization for binary sequences with high merit factors","authors":"Blaž Pšeničnik, Rene Mlinarič, Janez Brest, Borko Bošković","doi":"10.1016/j.dsp.2025.105316","DOIUrl":"10.1016/j.dsp.2025.105316","url":null,"abstract":"<div><div>The problem of finding aperiodic low auto-correlation binary sequences (LABS) presents a significant computational challenge, particularly as the sequence length increases. Such sequences have important applications in communication engineering, physics, chemistry, and cryptography. This paper introduces a dual-step algorithm for long binary sequences with high merit factors. The first step employs a parallel algorithm utilizing skew-symmetry and restriction classes to generate sequence candidates with merit factors above a predefined threshold. The second step uses a priority queue algorithm to refine these candidates further, searching the entire search space unrestrictedly. By combining GPU-based parallel computing and dual-step optimization, our approach has successfully identified best-known binary sequences for all lengths ranging from 450 to 527, with the exception of length 518, where the previous best-known merit factor value was matched with a different sequence. This hybrid method significantly outperforms traditional exhaustive and stochastic search methods, offering an efficient solution for finding long sequences with good merit factors.</div></div>","PeriodicalId":51011,"journal":{"name":"Digital Signal Processing","volume":"165 ","pages":"Article 105316"},"PeriodicalIF":2.9,"publicationDate":"2025-05-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144084009","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

UWEGAN: Enhancement of detailed features and restoration of image color UWEGAN：增强细节特征和恢复图像颜色

IF 2.9 3区工程技术

Digital Signal Processing Pub Date : 2025-05-13 DOI: 10.1016/j.dsp.2025.105324

Jinzhang Li, Jue Wang, Bo Li

{"title":"UWEGAN: Enhancement of detailed features and restoration of image color","authors":"Jinzhang Li, Jue Wang, Bo Li","doi":"10.1016/j.dsp.2025.105324","DOIUrl":"10.1016/j.dsp.2025.105324","url":null,"abstract":"<div><div>Underwater images are crucial in various domains, including marine science, resource exploration, ocean engineering, and underwater surveys. However, underwater images often suffer from issues such as detail loss, color distortion, and blurring due to complex water environments. To address these problems, this paper proposes a novel underwater image enhancement algorithm named UWEGAN, which combines a U-shaped encoder with a Generative Adversarial Network. The generator in UWEGAN integrates three key modules: the Multi-scale Feature Fusion Module (MFFM), the Feature Interaction Attention (FIA) module, and the Composite Residual Extraction Unit (CREU). Specifically, MFFM is designed to extract features from different spatial levels using parallel convolutions with varying kernel sizes and then fuses multi-scale global features to enhance the network?s representation capability. To correct color distortion, the FIA module models both channel-wise and pixel-wise relationships, enabling more targeted color adjustments and improving the overall color balance of the image. For image deblurring, the CREU replaces traditional convolution blocks with densely connected residual units that utilize deep residual learning and multi-level feature extraction strategies. This allows the network to effectively differentiate between noise and real structural information, thereby preserving image details. Extensive experiments conducted on public underwater datasets confirm that the proposed method significantly improves visual quality. Quantitative evaluations show that UWEGAN achieves average improvements of 2.33%, 2.12%, and 1.67% in PSNR, SSIM, and MSE, respectively, demonstrating its effectiveness in enhancing underwater images under challenging conditions.</div></div>","PeriodicalId":51011,"journal":{"name":"Digital Signal Processing","volume":"165 ","pages":"Article 105324"},"PeriodicalIF":2.9,"publicationDate":"2025-05-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144069922","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

Brain image denoising using dual-channel attentional residual network 基于双通道注意残差网络的脑图像去噪

IF 2.9 3区工程技术

Digital Signal Processing Pub Date : 2025-05-13 DOI: 10.1016/j.dsp.2025.105309

Huimin Qu, Haiyan Xie, Qianying Wang

引用次数: 0

A novel IMM Kalman filter with colored multi-outlier non-stationary heavy-tailed measurement noise and uncertain state model 一种新的带有有色多离群非平稳重尾测量噪声和不确定状态模型的IMM卡尔曼滤波器

IF 2.9 3区工程技术

Digital Signal Processing Pub Date : 2025-05-13 DOI: 10.1016/j.dsp.2025.105314

Runhua Yu , Sunyong Wu , Honggao Deng

{"title":"A novel IMM Kalman filter with colored multi-outlier non-stationary heavy-tailed measurement noise and uncertain state model","authors":"Runhua Yu , Sunyong Wu , Honggao Deng","doi":"10.1016/j.dsp.2025.105314","DOIUrl":"10.1016/j.dsp.2025.105314","url":null,"abstract":"<div><div>A novel interactive multi-model (IMM) Kalman filter is proposed in this paper for the dynamic estimation with colored multi-outlier non-stationary heavy-tailed measurement noise (MNHMN) and uncertain state model. Firstly, the filtering problem with colored MNHMN is converted to the filtering problem with white MNHMN using the measurement difference method and the state expansion approach. To fully fit the multi-outlier non-stationary heavy-tailed property of the white MNHMN, a generalized Gaussian–Student's t mixture (GSTM) distribution is proposed, through which each dimension of the noise is independently modeled as a GSTM distribution. Meanwhile, a multivariate Bernoulli variable is introduced to construct a hierarchical Gaussian model, while the variational Bayesian (VB) technique is used to estimate the system state and the distribution parameters of noise collectively. The IMM method is adopted to deal with state model uncertainty, and a new model conditional likelihood function based on the proposed measurement noise model is derived through the variational lower bound theory. Thus, the lack of an analytical likelihood in the proposed generalized GSTM distribution is effectively resolved. Simulation results demonstrate the effectiveness of the proposed method.</div></div>","PeriodicalId":51011,"journal":{"name":"Digital Signal Processing","volume":"165 ","pages":"Article 105314"},"PeriodicalIF":2.9,"publicationDate":"2025-05-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144115477","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

RGPNet: Learning a detail-preserved progressive model for single image deraining based on gradient prior RGPNet：学习一种基于梯度先验的单幅图像去噪的细节保留渐进模型

IF 2.9 3区工程技术

Digital Signal Processing Pub Date : 2025-05-13 DOI: 10.1016/j.dsp.2025.105312

Yu Luo , Gaoquan Liang , Jie Ling , Teng Zhou , Huiwu Huang , Tian Han

{"title":"RGPNet: Learning a detail-preserved progressive model for single image deraining based on gradient prior","authors":"Yu Luo , Gaoquan Liang , Jie Ling , Teng Zhou , Huiwu Huang , Tian Han","doi":"10.1016/j.dsp.2025.105312","DOIUrl":"10.1016/j.dsp.2025.105312","url":null,"abstract":"<div><div>The issue of rain removal in images is challenging due to the diverse rain distributions and complex backgrounds. Although deraining capabilities have greatly improved in the previous single-stage approaches, background detail loss is a common result. To effectively remove rain that is diversly distributed, many methods adopt a multistage rain removal framework for progressive deraining. Most of these multistage methods repeatedly use information from previous stages, or the original rainy image to refine the rain extracted from the background, leading to high demands on the ability to discriminate the background structure from the rain. To address this problem, we introduce a gradient prior to artificially preserve the background details. In addition, an attention LSTM is proposed to reduce the artifacts caused by over-deraining, by focusing on the more visible rain regions. The overall architecture of the proposed method consists of a rain streak extraction branch and a background detail recovery branch, with the designed attention LSTM and the proposed gradient prior integrated into the former and latter branches, respectively. Experiments on several well-known benchmark datasets show that our methods can outperform many state-of-the-art methods.</div></div>","PeriodicalId":51011,"journal":{"name":"Digital Signal Processing","volume":"165 ","pages":"Article 105312"},"PeriodicalIF":2.9,"publicationDate":"2025-05-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144115473","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

A gated leaky integrate-and-fire spiking neural network based on attention mechanism for multi-modal emotion recognition 基于注意机制的多模态情绪识别的门控泄漏集成-火峰神经网络

IF 2.9 3区工程技术

Digital Signal Processing Pub Date : 2025-05-13 DOI: 10.1016/j.dsp.2025.105322

Guoming Chen , Zhuoxian Qian , Shuang Qiu , Dong Zhang , Ruqi Zhou

{"title":"A gated leaky integrate-and-fire spiking neural network based on attention mechanism for multi-modal emotion recognition","authors":"Guoming Chen , Zhuoxian Qian , Shuang Qiu , Dong Zhang , Ruqi Zhou","doi":"10.1016/j.dsp.2025.105322","DOIUrl":"10.1016/j.dsp.2025.105322","url":null,"abstract":"<div><div>Multi-modal emotion recognition is a key research area in human-computer interaction. It involves processing heterogeneous multi-modal signals, which present challenges in signal alignment while aiming to enhance accuracy and reduce computational complexity. To address these challenges, we apply swarm decomposition to EEG signals to reduce noise and extract Short-Time Fourier Transform features. Heatmap features are then derived from these signals, as well as from other non-physiological signals such as facial expressions, voice, and text. These features from various sources are aligned using Discrete Wavelet Transform. We propose a Gated Leaky Integrate-and-Fire Spiking Convolutional Vision Transformer (GLIFCVT) framework for multimodal emotion recognition. This framework utilizes visual features as the primary modality and incorporates a spiking gated attention mechanism to enhance multimodal fusion and classification. In addition, we propose a novel loss function that integrates Focal and Dice losses to address class imbalance. Experiments demonstrate our proposed model consistently outperform state-of-the-art methods in both accuracy and energy efficiency.</div></div>","PeriodicalId":51011,"journal":{"name":"Digital Signal Processing","volume":"165 ","pages":"Article 105322"},"PeriodicalIF":2.9,"publicationDate":"2025-05-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144069924","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

FourCornerGAN: Glyph formation augmentation for unpaired Chinese font generation 对非配对中文字体生成的字形形成增强

IF 2.9 3区工程技术

Digital Signal Processing Pub Date : 2025-05-12 DOI: 10.1016/j.dsp.2025.105305

Kaili Wang, Chi Zhou, Yuanlin Shi, Tianquan Wu, Chen Chen

{"title":"FourCornerGAN: Glyph formation augmentation for unpaired Chinese font generation","authors":"Kaili Wang, Chi Zhou, Yuanlin Shi, Tianquan Wu, Chen Chen","doi":"10.1016/j.dsp.2025.105305","DOIUrl":"10.1016/j.dsp.2025.105305","url":null,"abstract":"<div><div>Chinese character font generation poses unique challenges due to the complexity of glyph structures and the scarcity of paired training data. Existing methods for Chinese character font generation often suffer from issues like missing glyph formation and insufficient detail. To overcome these limitations, combining with the spatial glyph formation information, a novel encoding method based on the Four-Corner Number is proposed and integrated into CycleGAN to develop into FourCornerGAN to enhance structural representation in unpaired Chinese font generation, and a new Four-Corner Consistency Loss is introduced to guide both the generator and discriminator in preserving spatial glyph formation details. Extensive experiments demonstrate that FourCornerGAN significantly improves generation quality over baseline models, particularly in structural accuracy and visual consistency. This approach offers a promising solution for high-fidelity font synthesis without paired samples. Code and dataset are available at <span><span>https://github.com/nini739/FourCornerGAN</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":51011,"journal":{"name":"Digital Signal Processing","volume":"165 ","pages":"Article 105305"},"PeriodicalIF":2.9,"publicationDate":"2025-05-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144084071","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

Improved proportionate affine projection sign algorithms for adaptive feedback cancellation using pre-filters in hearing aids 基于预滤波器的助听器自适应反馈抵消改进比例仿射投影符号算法

IF 2.9 3区工程技术

Digital Signal Processing Pub Date : 2025-05-12 DOI: 10.1016/j.dsp.2025.105299

Linh T.T. Tran , Felix Albu

{"title":"Improved proportionate affine projection sign algorithms for adaptive feedback cancellation using pre-filters in hearing aids","authors":"Linh T.T. Tran , Felix Albu","doi":"10.1016/j.dsp.2025.105299","DOIUrl":"10.1016/j.dsp.2025.105299","url":null,"abstract":"<div><div>An open-fitting hearing aid often experiences acoustic feedback, limiting the achievable amplification gain and degrading sound quality. Prediction-error-method-based adaptive feedback cancellation (PEM-AFC) is a widely recognized approach for mitigating the adverse effects of acoustic feedback. Proportionate-type algorithms combined with affine projection sign algorithms, known as PAPSA, along with its variants such as improved PAPSA (IPAPSA), memory IPAPSA (MIPAPSA), and block-sparse MIPAPSA (BS-MIPAPSA), have been successfully applied to network echo cancellation applications. However, using these fast adaptive algorithms for acoustic feedback cancellation remains limited due to the inherent correlation between the incoming and the loudspeaker signals. To address this challenge, we propose integrating these adaptive algorithms with PEM-AFC, resulting in a new class of AFC methods for hearing aids, including PEM-IPAPSA, PEM-MIPAPSA, and PEM-BSMIPAPSA. The proposed AFC methods leverage the pre-filter, the sparse nature of the feedback path, and fast adaptive filtering techniques to enhance convergence rate and tracking ability while maintaining similar steady-state error levels. We provide a detailed derivation of the proposed AFC methods and evaluate their performance using recorded speech as the incoming signal, with abrupt changes in the feedback path. Simulations were conducted in environments with/without background noise and impulsive noise. Simulation results show that the proposed methods are robust against impulsive interference and colored input, achieving higher convergence and tracking rates while maintaining similar steady-state errors compared to state-of-the-art competing methods. Additionally, the proposed methods offer low computational complexity, which is crucial for hearing aids where low power consumption is a significant concern.</div></div>","PeriodicalId":51011,"journal":{"name":"Digital Signal Processing","volume":"165 ","pages":"Article 105299"},"PeriodicalIF":2.9,"publicationDate":"2025-05-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143948873","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

Extended flipping CADiS array configuration for DOA estimation of non-circular signals 非圆信号DOA估计的扩展翻转CADiS阵列配置

IF 2.9 3区工程技术

Digital Signal Processing Pub Date : 2025-05-12 DOI: 10.1016/j.dsp.2025.105318

Xiaolong Li , Xiaofei Zhang , Hao Hu

引用次数: 0

Multi-geometric distance method for clutter covariance matrix estimation 杂波协方差矩阵估计的多几何距离方法

IF 2.9 3区工程技术

Digital Signal Processing Pub Date : 2025-05-12 DOI: 10.1016/j.dsp.2025.105313

Shuang Yu , Xiaolin Du , Wenming Ma , Jia Liu , Xingjie Wu

引用次数: 0