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Geometry-Aware RWKV for Heterogeneous Light Field Spatial Super-Resolution 基于几何感知的非均匀光场空间超分辨率RWKV
IF 3.2 2区 工程技术
IEEE Signal Processing Letters Pub Date : 2025-03-27 DOI: 10.1109/LSP.2025.3555445
Zean Chen;Yeyao Chen;Linwei Zhu;Haiyong Xu;Gangyi Jiang
{"title":"Geometry-Aware RWKV for Heterogeneous Light Field Spatial Super-Resolution","authors":"Zean Chen;Yeyao Chen;Linwei Zhu;Haiyong Xu;Gangyi Jiang","doi":"10.1109/LSP.2025.3555445","DOIUrl":"https://doi.org/10.1109/LSP.2025.3555445","url":null,"abstract":"Heterogeneous Light Field (LF) spatial Super-Resolution (SR) aims to significantly enhance the spatial resolution of LF imaging by integrating an extra 2D digital camera. Inspired by the Receptance Weighted Key Value (RWKV), a simple yet effective heterogeneous LF spatial SR method is proposed. Specifically, a texture transfer module with channel correlation is designed, which leverages a feature distillation strategy to transfer texture information from the high-resolution 2D image to the low-resolution LF image. Meanwhile, a spatial-angular rectification module is constructed to restore the spatial-angular coherence damaged in texture transfer. It employs geometry-aware RWKV to capture the intrinsic geometric structure of LFs. Experimental results show that the proposed method outperforms the state-of-the-art methods in both quantitative and qualitative comparisons, while achieving higher efficiency in terms of inference time and memory usage.","PeriodicalId":13154,"journal":{"name":"IEEE Signal Processing Letters","volume":"32 ","pages":"1795-1799"},"PeriodicalIF":3.2,"publicationDate":"2025-03-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143925253","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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
Watermark Removal Attack Against Text-to-Image Generative Model Watermarking 针对文本到图像生成模型水印的水印去除攻击
IF 3.2 2区 工程技术
IEEE Signal Processing Letters Pub Date : 2025-03-26 DOI: 10.1109/LSP.2025.3554514
Zihan Yuan;Li Li;Zichi Wang;Jingyuan Jiang;Xinpeng Zhang
{"title":"Watermark Removal Attack Against Text-to-Image Generative Model Watermarking","authors":"Zihan Yuan;Li Li;Zichi Wang;Jingyuan Jiang;Xinpeng Zhang","doi":"10.1109/LSP.2025.3554514","DOIUrl":"https://doi.org/10.1109/LSP.2025.3554514","url":null,"abstract":"The artist's style can be quickly imitated by fine-tuning a text-to-image model using artist's artworks, which raises serious copyright concerns. Scholars have proposed many watermarking methods to protect the artists' copyright. To evaluate the security and enhance the performance of existing watermarking, this paper proposes a watermark removal attack for text-to-image generative model watermarking for the first time. This attack aims to invalidate watermarking designed to detect art theft mimicry in text-to-image models. In this method, a watermark recognition network and a watermark removal network are designed. The watermark recognition network identifies whether an artwork contains watermark, and the watermark removal network is used to remove it. Consequently, text-to-image models fine-tuned with watermark-removed artworks can reproduce an artist's style while evading watermark detection. This makes the copyright authentication of artworks ineffective. Experiments show that the proposed attack can effectively remove watermarks, with watermark extraction accuracy dropping below 48.64%. Additionally, the images after watermark removal retain high similarity to the original images, with PSNR exceeding 27.96 and SSIM exceeding 0.92.","PeriodicalId":13154,"journal":{"name":"IEEE Signal Processing Letters","volume":"32 ","pages":"1470-1474"},"PeriodicalIF":3.2,"publicationDate":"2025-03-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143809027","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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
Receiver-Agnostic Radio Frequency Fingerprinting Using a Prototypical Contrastive Domain Adaptation Method 基于原型对比域自适应方法的接收机无关射频指纹识别
IF 3.2 2区 工程技术
IEEE Signal Processing Letters Pub Date : 2025-03-26 DOI: 10.1109/LSP.2025.3555099
Wenhan Li;Jiangong Wang;Taijun Liu;Gaoming Xu
{"title":"Receiver-Agnostic Radio Frequency Fingerprinting Using a Prototypical Contrastive Domain Adaptation Method","authors":"Wenhan Li;Jiangong Wang;Taijun Liu;Gaoming Xu","doi":"10.1109/LSP.2025.3555099","DOIUrl":"https://doi.org/10.1109/LSP.2025.3555099","url":null,"abstract":"Radio frequency (RF) fingerprinting is a technique used to identify different transmitters by analyzing the unique hardware impairments of RF transmitters, known as RF fingerprints. However, many existing studies have primarily focused on transmitter impairments, while the impact of receiver hardware impairments on RF signals has often been overlooked. To alleviate this issue, this letter proposes a receiver-agnostic RF fingerprinting method using unsupervised domain adaptation. The method employs prototypical contrastive learning to align the features of source domain and target domain data, while simultaneously learning the features of both domains. Experimental results on the real-world dataset (WiSig) demonstrate that the proposed method outperforms other receiver-agnostic RF fingerprinting methods.","PeriodicalId":13154,"journal":{"name":"IEEE Signal Processing Letters","volume":"32 ","pages":"1910-1914"},"PeriodicalIF":3.2,"publicationDate":"2025-03-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143937925","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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
Exploring Non-Matching Multiple References for Speech Quality Assessment 语音质量评价的非匹配多参考文献探索
IF 3.2 2区 工程技术
IEEE Signal Processing Letters Pub Date : 2025-03-26 DOI: 10.1109/LSP.2025.3555190
Bao Thang Ta;Nhat Minh Le;Huynh Thi Thanh Binh;Van Hai Do
{"title":"Exploring Non-Matching Multiple References for Speech Quality Assessment","authors":"Bao Thang Ta;Nhat Minh Le;Huynh Thi Thanh Binh;Van Hai Do","doi":"10.1109/LSP.2025.3555190","DOIUrl":"https://doi.org/10.1109/LSP.2025.3555190","url":null,"abstract":"Non-Matching Reference-based Speech Quality Assessment models typically require numerous references during inference to ensure stable and accurate predictions. However, this dependency introduces significant computational overhead, limiting their suitability for real-time applications. In this paper, we propose a novel training paradigm that directly addresses prediction instability at its source by integrating multiple references during training rather than during inference, as in existing approaches. This method allows the model to capture the inherent variability of reference signals, thereby enhancing prediction reliability. Additionally, we introduce an auxiliary variance loss function to minimize inconsistencies across predictions, ensuring stable assessments regardless of the number of references used. Experiments on the NISQA datasets demonstrate that, with the same training time, our method achieves consistent predictions with a single reference during inference, resulting in a 100-fold reduction in computational time while maintaining high accuracy.","PeriodicalId":13154,"journal":{"name":"IEEE Signal Processing Letters","volume":"32 ","pages":"1610-1614"},"PeriodicalIF":3.2,"publicationDate":"2025-03-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143871046","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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
Multi-IRS-Aided Localization for Next-Generation Wireless Networks in Fading Environments 衰落环境下下一代无线网络的多irs辅助定位
IF 3.2 2区 工程技术
IEEE Signal Processing Letters Pub Date : 2025-03-25 DOI: 10.1109/LSP.2025.3554038
Nasir Saeed
{"title":"Multi-IRS-Aided Localization for Next-Generation Wireless Networks in Fading Environments","authors":"Nasir Saeed","doi":"10.1109/LSP.2025.3554038","DOIUrl":"https://doi.org/10.1109/LSP.2025.3554038","url":null,"abstract":"The growing demand for location-based services (LBS) in complex environments has increased the need for precise and reliable user localization techniques. Traditional methods often face limitations in scenarios with few access points (APs) and non-line-of-sight (NLOS) propagation, resulting in reduced accuracy. This paper presents a novel localization framework that leverages multiple Intelligent Reflecting Surfaces (IRS) to address these challenges and improve positioning accuracy in constrained conditions. The proposed method employs multiple IRSs to enhance signal propagation, mitigating the effects of NLOS conditions and improving signal quality. A Maximum Likelihood Estimation (MLE) algorithm is used to estimate user positions, while the Cramér-Rao Lower Bound (CRLB) is derived to benchmark the theoretical accuracy. By utilizing the reconfigurable capabilities of IRSs, the system dynamically adjusts wireless channels to optimize localization performance. Performance evaluations under practical fading conditions demonstrate significant improvements in accuracy compared to traditional methods. The results highlight the effectiveness and robustness of the proposed framework in diverse environments, showcasing the potential of IRS technology for advanced localization applications.","PeriodicalId":13154,"journal":{"name":"IEEE Signal Processing Letters","volume":"32 ","pages":"1460-1464"},"PeriodicalIF":3.2,"publicationDate":"2025-03-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143808973","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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
NADH: A NTRU-Based Adaptive Data Hiding Scheme for Underwater Acoustic Sensor Networks NADH：一种基于ntrus的水声传感器网络自适应数据隐藏方案
IF 3.2 2区 工程技术
IEEE Signal Processing Letters Pub Date : 2025-03-24 DOI: 10.1109/LSP.2025.3554414
Ming Xu;Tongtong Guo
{"title":"NADH: A NTRU-Based Adaptive Data Hiding Scheme for Underwater Acoustic Sensor Networks","authors":"Ming Xu;Tongtong Guo","doi":"10.1109/LSP.2025.3554414","DOIUrl":"https://doi.org/10.1109/LSP.2025.3554414","url":null,"abstract":"To address the issue of data confidentiality and security in underwater acoustic sensor networks (UASNs), a NTRU-based Adaptive Data Hiding scheme called <sc>NADH</small> is proposed. The <sc>NADH</small> scheme is novel in two aspects. First, we propose a weighted interpolation approach based on information entropy to enhance both data security and embedding capacity. Second, we propose an adaptive coefficient selection mechanism to monitor environmental changes in real time and adjust the data embedding strategy to maximize embedding capacity. This letter also provides a theoretical analysis of the correctness and security of the NADH scheme, and proves the upper bound of its mean squared error (MSE). Experimental results show that when the embedding capacity of <sc>NADH</small> is 2048 bits, the MSE is 0.7495, the average peak signal-to-noise ratio (PSNR) is 49.792 dB, and the average structural similarity index (SSIM) is 0.9998, outperforming existing data hiding schemes.","PeriodicalId":13154,"journal":{"name":"IEEE Signal Processing Letters","volume":"32 ","pages":"1465-1469"},"PeriodicalIF":3.2,"publicationDate":"2025-03-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143809028","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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
Cooperative Vehicle Tracking in VANET Using a Distributed Improved Cubature Kalman Filter 基于分布式改进Cubature Kalman滤波的VANET协同车辆跟踪
IF 3.2 2区 工程技术
IEEE Signal Processing Letters Pub Date : 2025-03-24 DOI: 10.1109/LSP.2025.3553788
Xiaomei Qu;Tao Liu;Lei Mu;Wenrong Tan;Huanyan Jian
{"title":"Cooperative Vehicle Tracking in VANET Using a Distributed Improved Cubature Kalman Filter","authors":"Xiaomei Qu;Tao Liu;Lei Mu;Wenrong Tan;Huanyan Jian","doi":"10.1109/LSP.2025.3553788","DOIUrl":"https://doi.org/10.1109/LSP.2025.3553788","url":null,"abstract":"This letter addresses the issue of cooperative vehicle tracking in vehicular ad-hoc networks (VANETs) through the fusion of global navigation satellite system (GNSS) data and time-of-arrival (TOA) based ranging information. We propose a novel distributed improved Cubature Kalman Filter (CKF) to enhance the state estimation accuracy of all vehicles. This approach comprises two parts: local improved CKF processing and cooperative fusion tracking. Due to the nonlinearity of the ranging measurement function with respect to both local vehicle state and neighboring vehicle state, an augmented parameter vector is constructed in the improved CKF method to tackle this challenge. Then, we present the optimal cooperative fusion of the local vehicle state estimate and the estimates from its neighbors, in the sense of minimizing the fused mean squared error. Numerical examples demonstrate that the root of average mean squared error (RAMSE) of the proposed method can be significantly reduced.","PeriodicalId":13154,"journal":{"name":"IEEE Signal Processing Letters","volume":"32 ","pages":"1540-1544"},"PeriodicalIF":3.2,"publicationDate":"2025-03-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143845483","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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
Generating Image Counterfactuals in Deep Learning Models Without the Aid of Generative Models 在没有生成模型的帮助下，深度学习模型中生成图像反事实
IF 3.2 2区 工程技术
IEEE Signal Processing Letters Pub Date : 2025-03-24 DOI: 10.1109/LSP.2025.3554511
Ao Xu;Zihao Li;Yukai Zhang;Tieru Wu
{"title":"Generating Image Counterfactuals in Deep Learning Models Without the Aid of Generative Models","authors":"Ao Xu;Zihao Li;Yukai Zhang;Tieru Wu","doi":"10.1109/LSP.2025.3554511","DOIUrl":"https://doi.org/10.1109/LSP.2025.3554511","url":null,"abstract":"With the rapid development of artificial intelligence, particularly the rise of deep learning, the importance of Explainable Artificial Intelligence has become increasingly prominent. Among its key techniques, counterfactual explanation plays a crucial role in understanding the decision-making mechanisms of opaque models. However, the high dimensionality and complex feature patterns of image data pose significant challenges for the task of generating counterfactuals for images. Existing literature has proposed various algorithms based on different assumptions, many of which rely on the existence of appropriate generative models. Some of these assumptions, particularly the assumption regarding the existence of generative models, may be overly stringent. To address this issue, this letter introduces a novel assumption-free image counterfactual generation algorithm, DFO-S, based on Score Matching and gradient-free optimization techniques. The proposed method achieves high-quality counterfactual generation without relying on generative models. Through extensive empirical analysis, we demonstrate the significant superiority of our method in terms of performance.","PeriodicalId":13154,"journal":{"name":"IEEE Signal Processing Letters","volume":"32 ","pages":"1495-1499"},"PeriodicalIF":3.2,"publicationDate":"2025-03-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143830479","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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
An Optimal Hierarchical Arithmetic Average Fusion of GM-PHD Filters GM-PHD滤波器的最优层次算法平均融合
IF 3.2 2区 工程技术
IEEE Signal Processing Letters Pub Date : 2025-03-24 DOI: 10.1109/LSP.2025.3554142
Xue Yu;Feng Xi-An
{"title":"An Optimal Hierarchical Arithmetic Average Fusion of GM-PHD Filters","authors":"Xue Yu;Feng Xi-An","doi":"10.1109/LSP.2025.3554142","DOIUrl":"https://doi.org/10.1109/LSP.2025.3554142","url":null,"abstract":"We achieve the optimal Arithmetic Average (AA) fusion algorithm of Gaussian Mixture Probability Hypothesis Density (GM-PHD) filters in a hierarchical structure. First, the optimal single-target estimate fusion is derived, during which the prior estimate is indispensable. Then, the derived optimal estimate fusion is employed as the merging method of the AA fusion. A master filter dedicated to computing prior density is introduced, so our fusion algorithm features a hierarchical structure. Experiment results evidence our algorithm's optimality and superiority over the standard AA fusion.","PeriodicalId":13154,"journal":{"name":"IEEE Signal Processing Letters","volume":"32 ","pages":"1605-1609"},"PeriodicalIF":3.2,"publicationDate":"2025-03-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143870996","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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 Novel Third-Order Nested Array for DOA Estimation With Increased Degrees of Freedom 一种新的三阶嵌套阵列自由度增大的DOA估计
IF 3.2 2区 工程技术
IEEE Signal Processing Letters Pub Date : 2025-03-24 DOI: 10.1109/LSP.2025.3554599
Rajen Kumar Patra
{"title":"A Novel Third-Order Nested Array for DOA Estimation With Increased Degrees of Freedom","authors":"Rajen Kumar Patra","doi":"10.1109/LSP.2025.3554599","DOIUrl":"https://doi.org/10.1109/LSP.2025.3554599","url":null,"abstract":"In this work, a novel third-order nested array is proposed for direction-of-arrival (DOA) estimation, which achieves significantly more degrees of freedom (DOF) than the existing third-order and second-order nested arrays. It is known that for the DOA estimation with a third-order nested array, we can utilize the uniform lags of the third-order coarray (TOCA) of the array. In the proposed array, the elements are placed strategically so that the TOCA attains significant uniform lags. We provide the closed-form element locations of the proposed third-order nested array with the mathematical expression of the uniform lags of the TOCA. The proposed third-order nested array also suffers from significantly less mutual coupling as a framework is devised to place the elements in appropriate locations so that the mutual coupling will be substantially less. The required simulations are performed to show the advantages of the proposed third-order nested array over the existing third-order and second-order nested arrays.","PeriodicalId":13154,"journal":{"name":"IEEE Signal Processing Letters","volume":"32 ","pages":"1475-1479"},"PeriodicalIF":3.2,"publicationDate":"2025-03-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143902596","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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
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