Digital Signal Processing最新文献

A relocated augmented coprime array for balancing low mutual coupling and high degrees of freedom 一种用于平衡低互耦合和高自由度的重定位增广素数阵列

IF 3 3区工程技术

Digital Signal Processing Pub Date : 2026-04-15 Epub Date: 2026-01-31 DOI: 10.1016/j.dsp.2026.105972

Zifan Tao , Jun Yang , Xinping Chen

{"title":"A relocated augmented coprime array for balancing low mutual coupling and high degrees of freedom","authors":"Zifan Tao , Jun Yang , Xinping Chen","doi":"10.1016/j.dsp.2026.105972","DOIUrl":"10.1016/j.dsp.2026.105972","url":null,"abstract":"<div><div>As a sparsely array structure that has attracted much attention, the coprime array can provide high degrees of freedom and reduce the mutual coupling effect. However, there are holes in the difference co-array of the coprime array, which cannot fully utilize the array element information. This paper proposes a relocated augmented coprime array structure to fill the holes in the difference co-array. We determine the positions of sensors by analyzing the redundancy of the coprime array and combining it with a two-dimensional hole model. This design can significantly increase the degrees of freedom and uniform degrees of freedom, and the mutual coupling effect is also greatly reduced. We derive the closed-form expressions for the degrees of freedom, uniform degrees of freedom, and weight function of the proposed array. Simulation results demonstrate that, in comparison with current mainstream sparse array structures such as SNA, ICNA, SDNA and SSACA, the proposed RACA achieves uniform degrees of freedom comparable to those of SNA, exhibits excellent performance under both strong and weak mutual coupling scenarios, and strikes a balance between uDOF and mutual coupling effects, thus fully verifying its effectiveness.</div></div>","PeriodicalId":51011,"journal":{"name":"Digital Signal Processing","volume":"174 ","pages":"Article 105972"},"PeriodicalIF":3.0,"publicationDate":"2026-04-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146174809","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

Capturing HDR video in challenging light conditions by beam-splitting ratio variable multi-sensor system 利用分束比可变多传感器系统在恶劣光照条件下捕获HDR视频

IF 3 3区工程技术

Digital Signal Processing Pub Date : 2026-04-15 Epub Date: 2026-01-25 DOI: 10.1016/j.dsp.2026.105956

Zhangchi Qiao , Hongwei Yi , Desheng Wen , Yong Han

{"title":"Capturing HDR video in challenging light conditions by beam-splitting ratio variable multi-sensor system","authors":"Zhangchi Qiao , Hongwei Yi , Desheng Wen , Yong Han","doi":"10.1016/j.dsp.2026.105956","DOIUrl":"10.1016/j.dsp.2026.105956","url":null,"abstract":"<div><div>Recording video in HDR scenes is challenging because it is always limited by the potential well capacity and sampling rate of the imaging sensor. The essence of this problem is how to balance the relationship between temporal resolution, spatial resolution and dynamic range. To solve this, we designed a variable beam-splitting ratio multi-sensor system (BRVMS) to capture both long and short exposure frames. It consists of a variety of configurations to meet changing light conditions. In addition, we considered motion blur from long exposures before synthesising the HDR frames. We proposed a method to estimate the blur kernel using short exposure frame constraints and add a mask to remove outliers in the overexposed area. Finally, we proposed a match-fusion method based on the two-layer 3D patch (2L3DP) to generate high-quality, detail-rich HDR frames. Extensive experimental results and ablation studies were performed to show the effectiveness of the system. By combining the BRVMS with the 2L3DP match-fusion method, we have enhanced the adaptability and performance of the vision system in high-speed, high-dynamic-range scenes to meet the growing demands of vision applications.</div></div>","PeriodicalId":51011,"journal":{"name":"Digital Signal Processing","volume":"174 ","pages":"Article 105956"},"PeriodicalIF":3.0,"publicationDate":"2026-04-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146070885","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

Artifact-suppressed style transfer for Chinese ink paintings via enhanced CycleGAN 通过增强的CycleGAN研究中国水墨画的人工抑制风格转移

IF 3 3区工程技术

Digital Signal Processing Pub Date : 2026-04-15 Epub Date: 2026-01-29 DOI: 10.1016/j.dsp.2026.105965

Shuo Zhang, Shengwen Wang, Hongrui Liu, Yonghua Zhang, Ziqing Huang

{"title":"Artifact-suppressed style transfer for Chinese ink paintings via enhanced CycleGAN","authors":"Shuo Zhang, Shengwen Wang, Hongrui Liu, Yonghua Zhang, Ziqing Huang","doi":"10.1016/j.dsp.2026.105965","DOIUrl":"10.1016/j.dsp.2026.105965","url":null,"abstract":"<div><div>Style transfer, a pivotal domain in machine vision, has achieved remarkable success in generating Western-style paintings. However, due to the unique “void” (<em>Liubai</em>) aesthetic of Chinese ink painting, the direct application of existing methods often yields irregular artifacts in blank areas and washes out details of brush strokes. To mitigate these limitations, this paper proposes a physically-guided hierarchical attention framework based on CycleGAN. Specifically, we introduce a coarse-to-fine algorithmic design where an inverted brightness-based masking mechanism is first constructed to serve as a spatial prior, explicitly suppressing high-frequency artifacts in void regions based on physical domain characteristics. Building upon this spatial prior, the Convolutional Block Attention Module (CBAM) is integrated into the generator as an adaptive feature modulator, recalibrating weights to adaptively concentrate computational resources on refining semantic foreground textures. Additionally, we incorporate the Learned Perceptual Image Patch Similarity (LPIPS) metric into the cyclic consistency constraint. This perceptually aligned objective resolves the “texture smoothing” issue inherent in pixel-wise losses. Experiments on our curated L2I (Landscape-to-Ink) benchmark dataset show that the model effectively suppresses artifacts and enhances artistic effects, outperforming existing methods. This work offers a robust algorithmic solution for the preservation and innovation of traditional Chinese art. The dataset is available at <span><span>https://github.com/ww02711/L2I.git</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":51011,"journal":{"name":"Digital Signal Processing","volume":"174 ","pages":"Article 105965"},"PeriodicalIF":3.0,"publicationDate":"2026-04-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146174810","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

AmBC-NOMA with physical-layer network coding for mutualistic two-way relay cellular IoT 具有物理层网络编码的AmBC-NOMA，用于互助双向中继蜂窝物联网

IF 3 3区工程技术

Digital Signal Processing Pub Date : 2026-04-15 Epub Date: 2026-01-29 DOI: 10.1016/j.dsp.2026.105959

Youtao Jiang , Yao Xu , Shaobo Jia , Peng Lin , Xiaoxu Guo , Jianyue Zhu , Zhizhong Zhang

{"title":"AmBC-NOMA with physical-layer network coding for mutualistic two-way relay cellular IoT","authors":"Youtao Jiang , Yao Xu , Shaobo Jia , Peng Lin , Xiaoxu Guo , Jianyue Zhu , Zhizhong Zhang","doi":"10.1016/j.dsp.2026.105959","DOIUrl":"10.1016/j.dsp.2026.105959","url":null,"abstract":"<div><div>Non-orthogonal multiple access (NOMA)-based two-way relay (TWR) systems can enhance communication coverage and spectral efficiency, but they face challenges in supporting future cellular Internet of Things (IoT) due to the coexistence of heterogeneous rate signals. This paper proposes a mutualistic ambient backscatter communication-aided NOMA scheme for TWR-based cellular IoT, where two cellular users and a relaying user exchange information via physical-layer network coding and NOMA, while IoT devices transmit data using backscatter modulation and cellular radio frequency signals. However, the multi-type interference and complex composite channels in the proposed scheme result in complicated signal-to-interference-plus-noise ratio expressions, which complicate accurate performance characterization. To address this, we derive closed-form expressions for the ergodic sum rate (ESR) using an equivalent transformation of squared generalized-K random variables, and characterize the asymptotic ESR at high signal-to-noise ratio. Simulation results validate the theoretical analysis and demonstrate the ESR gains over conventional orthogonal multiple access, NOMA-based TWR, and symbiotic NOMA-based TWR, while revealing the impacts of the IoT device count, node distance, and power allocation on the ESR.</div></div>","PeriodicalId":51011,"journal":{"name":"Digital Signal Processing","volume":"174 ","pages":"Article 105959"},"PeriodicalIF":3.0,"publicationDate":"2026-04-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146174908","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

DiVOT: Differentiated interaction-guided video-level object tracking DiVOT：差异化交互引导视频级目标跟踪

IF 3 3区工程技术

Digital Signal Processing Pub Date : 2026-04-15 Epub Date: 2026-01-27 DOI: 10.1016/j.dsp.2026.105955

Zhixi Wu, Si Chen, Da-Han Wang, Shunzhi Zhu

{"title":"DiVOT: Differentiated interaction-guided video-level object tracking","authors":"Zhixi Wu, Si Chen, Da-Han Wang, Shunzhi Zhu","doi":"10.1016/j.dsp.2026.105955","DOIUrl":"10.1016/j.dsp.2026.105955","url":null,"abstract":"<div><div>Recent advancements in video-level methods have made significant strides in the object tracking field. This method leverages multiple online templates to capture rich temporal information. However, most existing methods treat online templates as equally important as the initial template, overlooking the inherent instability of online templates during updating, which consequently degrades tracking performance. To alleviate this issue, we propose a novel differentiated interaction-guided video-level object tracking method, termed DiVOT, aimed at mitigating the impact of template instability and boosting the tracking performance. Our feature extraction network consists of a differentiated encoder block, which differentially guides the interaction between the search region and various templates, enabling the tracker to achieve a balance between stability and adaptability. Additionally, we design an auxiliary module, i.e., the memory decoder, to compensate for the deficiency of the differentiated interaction, where the latency of online templates hinders the acquisition of the most recent target appearance information. Extensive experiments on six mainstream datasets, i.e., OTB100, GOT-10k, TrackingNet, VOT2020, NFS, and LaSOT, validate the effectiveness of our proposed method.</div></div>","PeriodicalId":51011,"journal":{"name":"Digital Signal Processing","volume":"174 ","pages":"Article 105955"},"PeriodicalIF":3.0,"publicationDate":"2026-04-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146174911","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

Multimodal enhanced underwater image generation method using flow matching model 基于流量匹配模型的多模态增强水下图像生成方法

IF 3 3区工程技术

Digital Signal Processing Pub Date : 2026-04-15 Epub Date: 2026-01-28 DOI: 10.1016/j.dsp.2026.105964

Haifeng Yu , Changxu Zhu , Ruicheng Zhang , Yankai Feng , Xinbin Li

{"title":"Multimodal enhanced underwater image generation method using flow matching model","authors":"Haifeng Yu , Changxu Zhu , Ruicheng Zhang , Yankai Feng , Xinbin Li","doi":"10.1016/j.dsp.2026.105964","DOIUrl":"10.1016/j.dsp.2026.105964","url":null,"abstract":"<div><div>Underwater Image Enhancement (UIE) methods and Underwater Object Detection (UOD) algorithms are used to monitor the growth of marine aquaculture organisms. However, compared to the original underwater image, image enhancement affects the accuracy of object detection. This paper proposes a Multimodal Enhanced Underwater Image Generation method based on flow matching (MEUIG) to generate enhanced underwater images containing object feature information. Firstly, a dual-branch flow matching model is designed which includes feature extraction branch and image enhancement branch. The feature extraction branch extracts the object feature information in the original underwater images. The enhanced underwater image in the image enhancement branch is achieved through the color-line method. Then, we proposed a fusion module to combine the information of the different modalities. This module fuses multimodal feature information which contains image generated by flow matching, feature information and enhanced image. Additionally, we construct a feature extraction module to extract the object features in the original image. Finally, a new loss function is designed, which considers the pixel movement path, the feature difference between the condition image and the output image and the reconstruction loss. Qualitative and quantitative evaluations show that MEUIG improves image quality while retaining the original information. Our method achieves significantly higher detection accuracy on YOLOv11 compared to existing underwater enhancement methods. In the detection of echinus, MEUIG method is 18.8% and 9.7% higher than the contrast enhancement method, respectively. The code of the MEUIG model and the 4889 dataset used for training the MEUIG model can be found at: <span><span>https://github.com/Warmth-0213/MEUIG.git</span><svg><path></path></svg></span>. The link of the 5455 underwater objects detection dataset is: <span><span>https://github.com/Warmth-0213/data1.git</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":51011,"journal":{"name":"Digital Signal Processing","volume":"174 ","pages":"Article 105964"},"PeriodicalIF":3.0,"publicationDate":"2026-04-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146174913","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

Hybrid transfer semantic segmentation architecture for hyperspectral image classification 高光谱图像分类的混合传递语义分割体系结构

IF 3 3区工程技术

Digital Signal Processing Pub Date : 2026-04-15 Epub Date: 2026-01-22 DOI: 10.1016/j.dsp.2025.105852

Huaiping Yan , Yupeng Hou , Chengcai Leng , Yilin Li , Yang Li

{"title":"Hybrid transfer semantic segmentation architecture for hyperspectral image classification","authors":"Huaiping Yan , Yupeng Hou , Chengcai Leng , Yilin Li , Yang Li","doi":"10.1016/j.dsp.2025.105852","DOIUrl":"10.1016/j.dsp.2025.105852","url":null,"abstract":"<div><div>Hyperspectral image (HSI) classification is a research hotspot in the field of remote sensing image processing. Deep learning-based methods have gradually become one of the mainstream in the field of HSI classification. However, deep learning-based HSI classification methods still face the challenge of insufficient training samples. Transfer learning is regarded as an effective method to alleviate the problem of insufficient samples. However, hyperspectral image data is scarce, lacking the foundation for pre-training high-quality models. In this paper, a Hybrid Transfer Semantic Segmentation Architecture (HTSSA) is proposed, which transfers knowledge from different datasets by adopting different network structures. The proposed model adopts a triple branch network architecture. The three branches respectively use the vision transformer (ViT) classification model pre-trained on ImageNet, the Deeplabv3 semantic segmentation model pre-trained on the PASCAL VOC 2012 dataset, and the convolutional neural network (CNN) model pre-trained on the source hyperspectral image dataset. The three branch network models were fine-tuned on the target hyperspectral image dataset. The mapping modules were designed to handle the problem of heterogeneous data migration. The ViT branch utilizes the Transformer to extract spatial global context features. The Deeplabv3 branch utilizes the feature pyramid to extract spatial local multi-scale features. The CNN branch uses 3D-CNN to extract the spectral features of hyperspectral images. Finally, the final classification result is obtained by using the fusion features of the three branches. Extensive experiments on public datasets have verified that the Hybrid Transfer Semantic Segmentation Architecture proposed in this paper has alleviated the negative impact of sample scarcity to a certain extent, enhanced the representation ability of the model, and improved the final classification performance.</div></div>","PeriodicalId":51011,"journal":{"name":"Digital Signal Processing","volume":"174 ","pages":"Article 105852"},"PeriodicalIF":3.0,"publicationDate":"2026-04-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146070884","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

KADNet:Low SNR automatic modulation classification via SNR aware deformable convolution and Kolmogorov-Arnold networks 低信噪比自动调制分类通过信噪比感知的可变形卷积和Kolmogorov-Arnold网络

IF 3 3区工程技术

Digital Signal Processing Pub Date : 2026-04-15 Epub Date: 2026-01-21 DOI: 10.1016/j.dsp.2026.105942

Run Wang , Jizhe Li , Youze Yang , Shasha Wang , Bing Zheng

{"title":"KADNet:Low SNR automatic modulation classification via SNR aware deformable convolution and Kolmogorov-Arnold networks","authors":"Run Wang , Jizhe Li , Youze Yang , Shasha Wang , Bing Zheng","doi":"10.1016/j.dsp.2026.105942","DOIUrl":"10.1016/j.dsp.2026.105942","url":null,"abstract":"<div><div>The proliferation of modern communication technologies has precipitated increasingly sophisticated electromagnetic environments, demanding more rigorous performance from Automatic Modulation Classification (AMC) systems, especially in low signal-to-noise ratio (SNR) scenarios where conventional approaches struggle with feature extraction and classification fidelity. In response, we propose KADNet, a novel architecture tailored for AMC in low-SNR scenarios.KADNet comprises two key components: a Signal Enhancement Module (SEM) and an SNR-Aware Deformable Convolutional Network (SADCN).In the SEM, time-domain I/Q samples are first projected into the frequency domain via the fast Fourier transform (FFT). A spectral weighting mask is then generated by a Kolmogorov-Arnold Network (KAN), enabling precise attenuation of noise and amplification of decision-relevant signal components. Subsequently, the SADCN employs a lightweight subnetwork to estimate a soft SNR map, which is then fused into deformable convolution operations via a Signal Quality Spatial Attention (SQSA) mechanism. This fusion produces secondary spatial offsets and modulation-adaptive weights, allowing sampling grids to adjust dynamically in response to local signal quality. Extensive experiments on the RADIOML 2016.10A/B benchmarks demonstrate the effectiveness of our design: KADNet achieves mean classification accuracies of 64.66 percent and 65.58 percent, corresponding to improvements of 2.04 percent and 0.56 percent over baseline methods. Moreover, within the extremely low-SNR range of -20 dB to -2 dB, KADNet attains average accuracies of 36.86 percent and 37.92 percent, surpassing the current state of the art by 3.0 percent to 3.8 percent. This significant improvement over the current state-of-the-art in the most challenging SNR conditions confirms that KADNet is a superior AMC method in low-SNR conditions.</div></div>","PeriodicalId":51011,"journal":{"name":"Digital Signal Processing","volume":"174 ","pages":"Article 105942"},"PeriodicalIF":3.0,"publicationDate":"2026-04-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146081608","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

WDCGAN-GSMR: A more accurate framework for small-sample radar signal modulation recognition WDCGAN-GSMR：一种更精确的小样本雷达信号调制识别框架

IF 3 3区工程技术

Digital Signal Processing Pub Date : 2026-04-15 Epub Date: 2026-01-31 DOI: 10.1016/j.dsp.2026.105971

Qinghui Zhang, Wenzheng Li, Chenxia Wan

{"title":"WDCGAN-GSMR: A more accurate framework for small-sample radar signal modulation recognition","authors":"Qinghui Zhang, Wenzheng Li, Chenxia Wan","doi":"10.1016/j.dsp.2026.105971","DOIUrl":"10.1016/j.dsp.2026.105971","url":null,"abstract":"<div><div>Low Probability of Interception (LPI) radars feature strong anti-detection capabilities, rendering the acquisition of real signal samples extremely challenging. This severely restricts the performance of LPI radar signal modulation recognition under small-sample conditions. To address this issue, this paper proposes a novel Wasserstein Deep Convolutional Generative Adversarial Network integrated with Generative Spatial-Channel Synergistic Attention and Multi-Scale Asymmetric Convolutional Residual (WDCGAN-GSMR), to enhance recognition accuracy under small-sample conditions. The radar signals are first transformed into Time-Frequency Images (TFIs) using the Smoothed Pseudo Wigner–Ville Distribution (SPWVD). These limited TFIs are then augmented using WDCGAN-GSMR by combining real-world and simulated samples, and are finally fed into a convolutional neural network for model training and modulation recognition. Experimental results demonstrate that incorporating the MCR block into WDCGAN-GSMR model significantly reduces the computational complexity. When only 50 samples per class are available, combining the proposed WDCGAN-GSMR with MobileNetV1 improves recognition accuracy by 6.2%. When integrated with the ResNet18 model, the recognition accuracy of the WDCGAN-GSMR model achieves a 6.4% higher than the conventional DCGAN model. This proposed model effectively mitigates the issue of data scarcity and significantly enhances LPI radar signal modulation recognition under small-sample conditions, providing a novel and effective solution for enhancing radar signal modulation recognition.</div></div>","PeriodicalId":51011,"journal":{"name":"Digital Signal Processing","volume":"174 ","pages":"Article 105971"},"PeriodicalIF":3.0,"publicationDate":"2026-04-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146174808","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

Enhanced underwater object tracking via adaptive image enhancement and multi-regularized correlation filters 通过自适应图像增强和多正则化相关滤波器增强水下目标跟踪

IF 3 3区工程技术

Digital Signal Processing Pub Date : 2026-04-15 Epub Date: 2026-01-28 DOI: 10.1016/j.dsp.2026.105958

Endong Liu, Lihui Wang

{"title":"Enhanced underwater object tracking via adaptive image enhancement and multi-regularized correlation filters","authors":"Endong Liu, Lihui Wang","doi":"10.1016/j.dsp.2026.105958","DOIUrl":"10.1016/j.dsp.2026.105958","url":null,"abstract":"<div><div>Underwater Object Tracking (UOT) is essential for underwater ecological monitoring, marine resource exploration, and autonomous underwater robotics, yet it remains challenging due to low visibility, illumination variations, visual aberrations, and severe color distortions. To address these issues, this paper proposes a task-driven underwater object tracking framework that tightly integrates selective image enhancement with a multi-regularized correlation filter. Specifically, an adaptive image enhancement strategy derived from the generalized Dark Channel Prior (DCP) is selectively activated using CCF indicators (colorfulness, contrast, and fog density), enabling effective visual enhancement while preserving real-time performance. On this basis, a multi-regularized correlation filter incorporating Gaussian-shaped spatial constraints and channel reliability weighting is formulated to improve robustness and localization accuracy under complex underwater conditions. The resulting optimization problem is efficiently solved within an ADMM framework. Extensive experiments on the UOT100 and UTB180 datasets demonstrate that the proposed method consistently outperforms state-of-the-art trackers, achieving superior precision and success rates in challenging underwater scenarios.</div></div>","PeriodicalId":51011,"journal":{"name":"Digital Signal Processing","volume":"174 ","pages":"Article 105958"},"PeriodicalIF":3.0,"publicationDate":"2026-04-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146174815","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