IEEE open journal of signal processing最新文献

{"title":"SeSeCo: Selective Semantic Compression of Images","authors":"Tom Bordin;Thomas Maugey;Sergio Barbarossa","doi":"10.1109/OJSP.2026.3667080","DOIUrl":"https://doi.org/10.1109/OJSP.2026.3667080","url":null,"abstract":"In image compression, in applications targeting extremely low bitrates (0.01 bpp), where the reconstruction distortion can be severe, it makes sense to prioritize parts of the image that are more relevant than others. In this paper, we propose a semantic compression framework that integrates user or application preferences to compress image parts based on their semantic representation. We design a guide for trained diffusion models that takes into account the preferences for describing objects with varying accuracies. We show that we are able to preserve the selected objects while also preserving the semantic and global aspect of the image without any retraining or fine-tuning.","PeriodicalId":73300,"journal":{"name":"IEEE open journal of signal processing","volume":"7 ","pages":"382-392"},"PeriodicalIF":2.7,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11406894","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147557648","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A Non-Contact ECG Reconstruction Method Based on FMCW Radar and Adaptive VMD-Assisted Deep Learning","authors":"Xiaolong Chen;Yonggang Luo;Long Zhang;Yinwei Li;Yiming Zhu","doi":"10.1109/OJSP.2026.3726823","DOIUrl":"https://doi.org/10.1109/OJSP.2026.3726823","url":null,"abstract":"To address the limitations of conventional ECG monitoring, including the need for direct skin contact, discomfort during long-term use, and limited applicability in special monitoring scenarios, this paper proposes a non-contact ECG reconstruction method based on FMCW radar and deep learning. In the signal preprocessing stage, adaptive Variational Mode Decomposition (VMD) optimized by Ant Colony Optimization (ACO) is introduced to suppress respiratory harmonic interference and improve heartbeat signal separation. In the reconstruction stage, a deep learning framework integrating convolutional neural networks, multi-head attention, and LSTM is developed to capture local temporal features and global sequential dependencies, enabling the mapping from radar-derived heartbeat signals to ECG waveforms. Experiments on a public dataset and additional hospital-collected data demonstrate that the proposed method can reconstruct ECG signals with high waveform consistency relative to reference ECGs. Quantitative results show that the reconstructed signals achieve low RMSE, high PCC, and good RR-interval agreement with the ground-truth ECG. The results indicate the feasibility of the proposed method for non-contact ECG reconstruction and suggest its potential for continuous physiological monitoring.","PeriodicalId":73300,"journal":{"name":"IEEE open journal of signal processing","volume":"7 ","pages":"874-888"},"PeriodicalIF":2.6,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11663404","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148925205","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A Data-Driven Underwater Acoustic Channel Simulator Based on Variable-Parameter Autoregressive Modeling","authors":"Biao Liu;Ning Jia;Biao Wang;Yonglin Zhang","doi":"10.1109/OJSP.2026.3742736","DOIUrl":"https://doi.org/10.1109/OJSP.2026.3742736","url":null,"abstract":"For testing underwater communication performance, model-driven channel simulators have high computational complexity, and the required dynamic environmental parameters are difficult to provide, making it challenging to generate realistic time-varying channels. In contrast, data-driven simulators do not suffer from these problems. Traditional underwater acoustic channel replay methods employ time-varying impulse responses; however, herein, a time-varying transfer function (TVTF) is employed to overcome the complexities associated with the large number of scattered acoustic paths in the time-delay domain. Singular value decomposition (SVD) is employed to implement stochastic acoustic channel replay by decomposing TVTF into principal and random components. A new random component can be generated randomly via the inverse process of the variable-parameter autoregressive (VPAR) model. To solve VPAR model, basis functions were used to translate frequency-dependent coefficients into constant coefficients, which can be estimated via a least square algorithm. SVD-VPAR replay (SVD-VPARR) method was proposed to generate realistic channel samples by inputting different complex noises into the inverse process of VPAR. The proposed simulator is validated using two measured channels from the WATERMARK benchmark and is compared with direct replay and an empirical mode decomposition-based stochastic replay method. Local time-frequency statistics and binary phase-shift keying / orthogonal frequency-division multiplexing communication results show that SVD-VPARR preserves the principal frequency-dependent channel characteristics and the major bit-error-rate variations with signal-to-noise ratio, time, and frequency.","PeriodicalId":73300,"journal":{"name":"IEEE open journal of signal processing","volume":"7 ","pages":"1062-1075"},"PeriodicalIF":2.6,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11733831","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149249288","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"High-Resolution Spectrograms","authors":"Milton J. Porsani;Bjørn Ursin","doi":"10.1109/OJSP.2026.3739210","DOIUrl":"https://doi.org/10.1109/OJSP.2026.3739210","url":null,"abstract":"We present a two-step method for computing the High-Resolution Spectrogram (HRS) of nonstationary signals. In the first step, a Weighted Short-Term Fourier Transform decomposes the signal using a bank of symmetric, tapered convolution operators, and the Hilbert transform extends each resulting component to its analytic form. In the second step, a single partial fraction decomposition is applied, corresponding to a first-order prediction error operator whose single pole is the first-order Burg reflection coefficient. A maximum-entropy autoregressive estimator then gives the instantaneous frequency of each locally monochromatic component within a sliding time window. The energy of each component is reassigned along the frequency axis to the pole location, producing sharply focused energy tracks and substantially improved time-frequency resolution. Summing all reassigned components reconstructs the original signal, which confirms invertibility and enables signal separation, denoising, and feature extraction. A systematic sensitivity analysis characterizes the method's valid operating range and parameter trade-offs, and quantitative evaluations show its robustness to white, colored, and impulsive noise across multiple SNR levels. Benchmarks against established high-resolution tools show competitive performance. The method is validated on synthetic signals, including a crossing chirp and a multi-component signal with stationary, frequency-modulated, swept, and impulsive constituents. It is then applied to a marine seismic trace and to the GW150914 gravitational wave signal recorded by the LIGO Hanford detector, demonstrating its effectiveness across a broad range of signal types and application domains.","PeriodicalId":73300,"journal":{"name":"IEEE open journal of signal processing","volume":"7 ","pages":"1043-1061"},"PeriodicalIF":2.6,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11717618","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149249541","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Consistent Zero-Shot Video Editing With Multimodal Feature Flow","authors":"Aviad Dahan;Eyal Gomel;Lior Wolf;Raja Giryes","doi":"10.1109/OJSP.2026.3691029","DOIUrl":"https://doi.org/10.1109/OJSP.2026.3691029","url":null,"abstract":"We present a novel zero-shot video editing framework that extends pre-trained text-to-image (T2I) models with optical flow coherence enforcement to achieve temporally consistent and high-quality video edits. By seamlessly integrating optical flow into the attention mechanism, our method propagates edits across frames, effectively addressing challenges such as motion and deformation while minimizing artifacts common in frame-wise editing. A key innovation of our approach is its ability to condition edits on both text prompts and reference images, enabling precise and flexible control over style and content. It supports diverse editing tasks, including text-based editing, image-to-video style transfer, and a combination of the two. Compared to existing (text-only) video editing tools, it improves temporal coherence and visual fidelity significantly, achieving state-of-the-art results in zero-shot settings. Our code is available at <uri>https://github.com/AviadDahan/MFF</uri>.","PeriodicalId":73300,"journal":{"name":"IEEE open journal of signal processing","volume":"7 ","pages":"531-540"},"PeriodicalIF":2.7,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11509357","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148003399","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"LeadKAN: A Low-Rank Kernelized Kolmogorov–Arnold Network for Efficient Nonlinear Representation of Electrocardiogram Signals","authors":"Mohammed Yusuf Ansari;MD Rabiul Islam;Mohammed Ishaq;Ibrahim Al-Muteb;Mohammed Yaqoob;Eduardo Feo-Flushing;Wajid Yousuf;Sarada Prasad Dakua;Marwa Qaraqe","doi":"10.1109/OJSP.2026.3690409","DOIUrl":"https://doi.org/10.1109/OJSP.2026.3690409","url":null,"abstract":"Electrocardiography (ECG) is a widely used, non-invasive tool for assessing cardiac function, but conventional disease-centric models do not fully capture overall cardiovascular health. Recent work has introduced the concept of ECG age: a neural network–predicted age derived from ECG signals. Its difference from chronological age, known as delta age (<inline-formula><tex-math>$Delta$</tex-math></inline-formula>Age), has emerged as a surrogate marker of cardiovascular well-being. While deep learning approaches have shown promise for ECG age estimation, their computational complexity and lack of interpretability limit deployment in compute-constrained clinical environments. Kolmogorov–Arnold Networks (KANs) offer parameter efficiency and improved interpretability, yet existing variants remain compute-heavy, underexplored for regression tasks, and unable to disentangle contributions from individual ECG leads. To address these challenges, we propose LeadKAN, a lightweight and explainable KAN architecture for ECG age estimation. LeadKAN is built on LoRKAN layers, a novel layer design that replaces fully connected layers with low-rank bilinear mixing followed by an RBF-kernelized top, significantly reducing parameter count and computation. LeadKAN achieves ECG age estimation performance (MSE <inline-formula><tex-math>$approx$</tex-math></inline-formula> 112; MAE <inline-formula><tex-math>$approx$</tex-math></inline-formula> 8.25 years) comparable to state-of-the-art models, while requiring 16× fewer parameters and 45× fewer multiply–add operations. Additionally, lead-specific encoders enable attribution analysis, thereby enhancing clinical interpretability. These results position LeadKAN as an efficient and explainable framework for ECG age estimation, with strong potential for deployment in real-world, compute-limited settings.","PeriodicalId":73300,"journal":{"name":"IEEE open journal of signal processing","volume":"7 ","pages":"612-620"},"PeriodicalIF":2.7,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11548621","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148236200","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Allpass-Based Chirp Design With Perfect Pulse Compression Property","authors":"Ivan W. Selesnick;Jun Li;Ryan Wu;Baokun Liu","doi":"10.1109/OJSP.2026.3707061","DOIUrl":"https://doi.org/10.1109/OJSP.2026.3707061","url":null,"abstract":"Chirp waveforms are widely used in radar, sonar, acoustics, and other sensing systems due to their desirable pulse-compression behavior. However, standard chirp signals like linear frequency modulation (LFM) waveforms inevitably produce sidelobes that limit detection performance. In this paper, we propose a unified allpass-based chirp design (ABCD) framework that generates chirp-like waveforms with the perfect pulse compression (PPC) property, i.e., their autocorrelation reduces exactly to an impulse. The key idea is to design an allpass IIR system whose impulse response resembles a chirp. The allpass structure guarantees zero sidelobes, while its phase response closely approximates the ideal LFM. Band-limited chirp waveforms and long-duration, high–time-bandwidth signals can be obtained via interpolation and cascading, while preserving the perfect pulse-compression property. Compared with existing windowing or nonlinear FM techniques, the proposed method achieves exact sidelobe elimination together with flexible control of waveform characteristics, offering clear advantages for high-resolution and high–dynamic range sensing applications.","PeriodicalId":73300,"journal":{"name":"IEEE open journal of signal processing","volume":"7 ","pages":"704-716"},"PeriodicalIF":2.6,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11576568","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148478926","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Recursive$mathcal { , L}_{2}$-to-$mathcal {L}_infty$ Filtering of Disturbed Models Based on the Backward Euler Method","authors":"Oscar G. Ibarra-Manzano;Shunyi Zhao;Yuan Xu;Yuriy S. Shmaliy","doi":"10.1109/OJSP.2026.3731083","DOIUrl":"https://doi.org/10.1109/OJSP.2026.3731083","url":null,"abstract":"When a system operates under impulsive disturbances and conventional state estimation methods become ineffective, robust peak error reduction is required. In this paper, the <italic>error-to-error transfer function</i> approach is used to compute the bias correction gain <inline-formula><tex-math>$mathbf {K}$</tex-math></inline-formula> for a robust recursive <inline-formula><tex-math>$mathcal {L}_{2}$</tex-math></inline-formula>-to-<inline-formula><tex-math>$mathcal {L}_infty$</tex-math></inline-formula> filter applied to discrete-time state-space models based on the backward Euler method. The disturbance is treated as a Gauss-Markov sequence, and the gain <inline-formula><tex-math>$mathbf {K}$</tex-math></inline-formula> is computed using the <italic>energy-to-peak</i> lemma, a newly formulated theorem, and a linear matrix inequality. The performances of the <inline-formula><tex-math>$mathcal {L}_{2}$</tex-math></inline-formula>-to-<inline-formula><tex-math>$mathcal {L}_infty$</tex-math></inline-formula>, <inline-formula><tex-math>$H_infty$</tex-math></inline-formula>, unbiased finite impulse response (UFIR), and Kalman filters are compared numerically based on the quasi periodic harmonic model in terms of mean square error, robustness, and estimation quality. An experimental verification is provided for air quality monitoring in a significantly polluted city area. It is shown that the gain <inline-formula><tex-math>$mathbf {K}$</tex-math></inline-formula> of the <inline-formula><tex-math>$mathcal {L}_{2}$</tex-math></inline-formula>-to-<inline-formula><tex-math>$mathcal {L}_infty$</tex-math></inline-formula> filter obeys the previously formulated rule of thumb, i.e. it ranges between the gains of the UFIR and Kalman filters. In both numerical and experimental examples, the <inline-formula><tex-math>$mathcal {L}_{2}$</tex-math></inline-formula>-to-<inline-formula><tex-math>$mathcal {L}_infty$</tex-math></inline-formula> filter exhibited the smallest peak errors.","PeriodicalId":73300,"journal":{"name":"IEEE open journal of signal processing","volume":"7 ","pages":"934-943"},"PeriodicalIF":2.6,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11680017","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149033819","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Adaptability of Vision Foundation Models for 3D Medical Image Segmentation","authors":"Suhyun Ahn;Donggyu Lee;Jinah Park","doi":"10.1109/OJSP.2025.3650437","DOIUrl":"https://doi.org/10.1109/OJSP.2025.3650437","url":null,"abstract":"Vision Foundation Models (VFMs), such as DINOv2 and SAM, have demonstrated unprecedented generalizability in natural imaging and show strong promise in medical imaging due to their semantically rich representations. However, their effective application to 3D volumetric segmentation remains largely underexplored, especially concerning optimal adaptation strategies for transferring 2D pre-trained knowledge to the structurally disparate 3D domain. To address this, we present a comprehensive investigation into the transferability and task-specific adaptability of six diverse 2D VFMs (including Self-Supervised, Vision-Language, and Segmentation Generalists) for 3D medical image segmentation. We systematically evaluate four distinct transfer learning paradigms, including advanced Fine-Tuning methods, across four heterogeneous 3D medical datasets. Our results establish VFMs as a powerful and cost-effective generalist baseline, consistently outperforming non-pretrained and standard 3D ViT architectures despite the substantial domain shift. Crucially, our systematic exploration reveals that parameter-efficient fine-tuning achieves the highest segmentation accuracy across all datasets. Feature-level analyses using PCA and CKA provide key insights, confirming that optimal performance stems from successfully balancing the preservation of generalizable low-level visual features with the adaptation of high-level, task-specific semantics.","PeriodicalId":73300,"journal":{"name":"IEEE open journal of signal processing","volume":"7 ","pages":"82-90"},"PeriodicalIF":2.7,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11321196","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146082097","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Neural Rate-Adaptive LDPC Decoding for the Slepian-Wolf Problem","authors":"Brent De Weerdt;Nikos Deligiannis","doi":"10.1109/OJSP.2026.3684426","DOIUrl":"https://doi.org/10.1109/OJSP.2026.3684426","url":null,"abstract":"Distributed source coding (DSC) enables efficient compression of correlated sources by performing independent encoding and joint decoding. Slepian-Wolf coding plays a central role in DSC, as it allows near-lossless compression of correlated data at rates asymptotically approaching the joint entropy.Traditional approaches to constructive Slepian-Wolf coding employ algebraic binning techniques using channel codes, with decoding performed via algorithms such as belief propagation (BP). While effective, these methods suffer from high computational cost and suboptimal compression performance. We propose the first constructive learned Slepian-Wolf decoder for rate-adaptive coding, using a single multi-rate Transformer model. The architecture is inspired by neural channel decoding, but addresses the unique challenges of syndrome-based Slepian-Wolf coding based on side information. Furthermore, we incorporate the proposed rate-adaptive neural Slepian-Wolf decoder into a novel neural layered Wyner-Ziv code design for the quadratic Gaussian case and into a new layered Wyner-Ziv design for distributed stereo image coding. For Slepian-Wolf coding of binary sources, our neural decoder improves the compression performance over traditional BP decoding by up to 11%. In our monolithic and layered Wyner-Ziv designs, we are between 0.05 and 0.2 bits/sample away from the estimated ideal rate bound, while entropy coding needs an additional 0.4-1.2 bits/sample compared to the proposed Slepian-Wolf codec. Moreover, our stereo image coding design reduces the coding rate by 9-19% in low rate settings compared to the state-of-the-art with minimal loss in image quality. Finally, the proposed decoder is 15 times faster than BP decoding on a GPU.","PeriodicalId":73300,"journal":{"name":"IEEE open journal of signal processing","volume":"7 ","pages":"456-471"},"PeriodicalIF":2.7,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11482822","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147828808","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}