IEEE open journal of signal processing最新文献

{"title":"Enhancing Learning-Based Cross-Modality Prediction for Lossless Medical Imaging Compression","authors":"Daniel S. Nicolau;Lucas A. Thomaz;Luis M. N. Tavora;Sergio M. M. Faria","doi":"10.1109/OJSP.2025.3564830","DOIUrl":"https://doi.org/10.1109/OJSP.2025.3564830","url":null,"abstract":"Multimodal medical imaging, which involves the simultaneous acquisition of different modalities, enhances diagnostic accuracy and provides comprehensive visualization of anatomy and physiology. However, this significantly increases data size, posing storage and transmission challenges. Standard image codecs fail to properly exploit cross-modality redundancies, limiting coding efficiency. In this paper, a novel approach is proposed to enhance the compression gain and to reduce the computational complexity of a lossless cross-modality coding scheme for multimodal image pairs. The scheme uses a deep learning-based approach with Image-to-Image translation based on a Generative Adversarial Network architecture to generate an estimated image of one modality from its cross-modal pair. Two different approaches for inter-modal prediction are considered: one using the original and the estimated images for the inter-prediction scheme and another considering a weighted sum of both images. Subsequently, a decider based on a Convolutional Neural Network is employed to estimate the best coding approach to be selected among the two alternatives, before the coding step. A novel loss function that considers the decision accuracy and the compression gain of the chosen prediction approach is applied to improve the decision-making task. The experimental results on PET-CT and PET-MRI datasets demonstrate that the proposed approach improves by 11.76% and 4.61% the compression efficiency when compared with the single modality intra-coding of the Versatile Video Coding. Additionally, this approach allows to reduce the computational complexity by almost half in comparison to selecting the most compression-efficient after testing both schemes.","PeriodicalId":73300,"journal":{"name":"IEEE open journal of signal processing","volume":"6 ","pages":"489-497"},"PeriodicalIF":2.9,"publicationDate":"2025-04-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10978054","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143943910","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":"Content-Adaptive Inference for State-of-the-Art Learned Video Compression","authors":"Ahmet Bilican;M. Akın Yılmaz;A. Murat Tekalp","doi":"10.1109/OJSP.2025.3564817","DOIUrl":"https://doi.org/10.1109/OJSP.2025.3564817","url":null,"abstract":"While the BD-rate performance of recent learned video codec models in both low-delay and random-access modes exceed that of respective modes of traditional codecs on average over common benchmarks, the performance improvements for individual videos with complex/large motions is much smaller compared to scenes with simple motion. This is related to the inability of a learned encoder model to generalize to motion vector ranges that have not been seen in the training set, which causes loss of performance in both coding of flow fields as well as frame prediction and coding. As a remedy, we propose a generic (model-agnostic) framework to control the scale of motion vectors in a scene during inference (encoding) to approximately match the range of motion vectors in the test and training videos by adaptively downsampling frames. This results in down-scaled motion vectors enabling: i) better flow estimation; hence, frame prediction and ii) more efficient flow compression. We show that the proposed framework for content-adaptive inference improves the BD-rate performance of already state-of-the-art low-delay video codec DCVC-FM by up to 41% on individual videos without any model fine tuning. We present ablation studies to show measures of motion and scene complexity can be used to predict the effectiveness of the proposed framework.","PeriodicalId":73300,"journal":{"name":"IEEE open journal of signal processing","volume":"6 ","pages":"498-506"},"PeriodicalIF":2.9,"publicationDate":"2025-04-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10978087","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143943980","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":"Adversarial Robustness of Self-Supervised Learning Features","authors":"Nicholas Mehlman;Shri Narayanan","doi":"10.1109/OJSP.2025.3562797","DOIUrl":"https://doi.org/10.1109/OJSP.2025.3562797","url":null,"abstract":"As deep learning models have proliferated, concerns about their reliability and security have also increased. One significant challenge is understanding adversarial perturbations, which can alter a model's predictions despite being very small in magnitude. Prior work has proposed that this phenomenon results from a fundamental deficit in supervised learning, by which classifiers exploit whatever input features are more predictive, regardless of whether or not these features are robust to adversarial attacks. In this paper, we consider feature robustness in the context of contrastive self-supervised learning methods that have become especially common in recent years. Our findings suggest that the features learned during self-supervision are, in fact, more resistant to adversarial perturbations than those generated from supervised learning. However, we also find that these self-supervised features exhibit poorer inter-class disentanglement, limiting their contribution to overall classifier robustness.","PeriodicalId":73300,"journal":{"name":"IEEE open journal of signal processing","volume":"6 ","pages":"468-477"},"PeriodicalIF":2.9,"publicationDate":"2025-04-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10971198","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143908351","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":"Array Design for Angle of Arrival Estimation Using the Worst-Case Two-Target Cramér-Rao Bound","authors":"Costas A. Kokke;Mario Coutino;Richard Heusdens;Geert Leus","doi":"10.1109/OJSP.2025.3558686","DOIUrl":"https://doi.org/10.1109/OJSP.2025.3558686","url":null,"abstract":"Sparse array design is used to help reduce computational, hardware, and power requirements compared to uniform arrays while maintaining acceptable performance. Although minimizing the Cramér-Rao bound has been adopted previously for sparse sensing, it did not consider multiple targets and unknown target directions. To handle the unknown target directions when optimizing the Cramér-Rao bound, we propose to use the worst-case Cramér-Rao bound of two uncorrelated equal power sources with arbitrary angles. This new worst-case two-target Cramér-Rao bound metric has some resemblance to the peak sidelobe level metric which is commonly used in unknown multi-target scenarios. We cast the sensor selection problem for 3-D arrays using the worst-case two-target Cramér-Rao bound as a convex semi-definite program and obtain the binary selection by randomized rounding. We illustrate the proposed method through numerical examples, comparing it to solutions obtained by minimizing the single-target Cramér-Rao bound, minimizing the Cramér-Rao bound for known target angles, the concentric rectangular array and the boundary array. We show that our method selects a combination of edge and center elements, which contrasts with solutions obtained by minimizing the single-target Cramér-Rao bound. The proposed selections also exhibit lower peak sidelobe levels without the need for sidelobe level constraints.","PeriodicalId":73300,"journal":{"name":"IEEE open journal of signal processing","volume":"6 ","pages":"453-467"},"PeriodicalIF":2.9,"publicationDate":"2025-04-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10955272","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143896495","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":"Unified Analysis of Decentralized Gradient Descent: A Contraction Mapping Framework","authors":"Erik G. Larsson;Nicolò Michelusi","doi":"10.1109/OJSP.2025.3557332","DOIUrl":"https://doi.org/10.1109/OJSP.2025.3557332","url":null,"abstract":"The decentralized gradient descent (DGD) algorithm, and its sibling, diffusion, are workhorses in decentralized machine learning, distributed inference and estimation, and multi-agent coordination. We propose a novel, principled framework for the analysis of DGD and diffusion for strongly convex, smooth objectives, and arbitrary undirected topologies, using contraction mappings coupled with a result called the mean Hessian theorem (MHT). The use of these tools yields tight convergence bounds, both in the noise-free and noisy regimes. While these bounds are qualitatively similar to results found in the literature, our approach using contractions together with the MHT decouples the algorithm dynamics (how quickly the algorithm converges to its fixed point) from its asymptotic convergence properties (how far the fixed point is from the global optimum). This yields a simple, intuitive analysis that is accessible to a broader audience. Extensions are provided to multiple local gradient updates, time-varying step sizes, noisy gradients (stochastic DGD and diffusion), communication noise, and random topologies.","PeriodicalId":73300,"journal":{"name":"IEEE open journal of signal processing","volume":"6 ","pages":"507-529"},"PeriodicalIF":2.9,"publicationDate":"2025-04-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10947567","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144117149","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":"VAMP-Based Kalman Filtering Under Non-Gaussian Process Noise","authors":"Tiancheng Gao;Mohamed Akrout;Faouzi Bellili;Amine Mezghani","doi":"10.1109/OJSP.2025.3557271","DOIUrl":"https://doi.org/10.1109/OJSP.2025.3557271","url":null,"abstract":"Estimating time-varying signals becomes particularly challenging in the face of non-Gaussian (e.g., sparse) and/or rapidly time-varying process noise. By building upon the recent progress in the approximate message passing (AMP) paradigm, this paper unifies the vector variant of AMP (i.e., VAMP) with the Kalman filter (KF) into a unified message passing framework. The new algorithm (coined VAMP-KF) does not restrict the process noise to a specific structure (e.g., same support over time), thereby accounting for non-Gaussian process noise sources that are uncorrelated both component-wise and over time. For the sake of theoretical performance prediction, we conduct a state evolution (SE) analysis of the proposed algorithm and show its consistency with the asymptotic empirical mean-squared error (MSE). Numerical results using sparse noise dynamics with different sparsity ratios demonstrate unambiguously the effectiveness of the proposed VAMP-KF algorithm and its superiority over state-of-the-art algorithms both in terms of reconstruction accuracy and computational complexity.","PeriodicalId":73300,"journal":{"name":"IEEE open journal of signal processing","volume":"6 ","pages":"434-452"},"PeriodicalIF":2.9,"publicationDate":"2025-04-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10947573","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143908383","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":"Adaptive Anomaly Detection in Network Flows With Low-Rank Tensor Decompositions and Deep Unrolling","authors":"Lukas Schynol;Marius Pesavento","doi":"10.1109/OJSP.2025.3549350","DOIUrl":"https://doi.org/10.1109/OJSP.2025.3549350","url":null,"abstract":"Anomaly detection (AD) is increasingly recognized as a key component for ensuring the resilience of future communication systems. While deep learning has shown state-of-the-art AD performance, its application in critical systems is hindered by concerns regarding training data efficiency, domain adaptation and interpretability. This work considers AD in network flows using incomplete measurements, leveraging a robust tensor decomposition approach and deep unrolling techniques to address these challenges. We first propose a novel block-successive convex approximation algorithm based on a regularized model-fitting objective where the normal flows are modeled as low-rank tensors and anomalies as sparse. An augmentation of the objective is introduced to decrease the computational cost. We apply deep unrolling to derive a novel deep network architecture based on our proposed algorithm, treating the regularization parameters as learnable weights. Inspired by Bayesian approaches, we extend the model architecture to perform online adaptation to per-flow and per-time-step statistics, improving AD performance while maintaining a low parameter count and preserving the problem's permutation equivariances. To optimize the deep network weights for detection performance, we employ a homotopy optimization approach based on an efficient approximation of the area under the receiver operating characteristic curve. Extensive experiments on synthetic and real-world data demonstrate that our proposed deep network architecture exhibits a high training data efficiency, outperforms reference methods, and adapts seamlessly to varying network topologies.","PeriodicalId":73300,"journal":{"name":"IEEE open journal of signal processing","volume":"6 ","pages":"417-433"},"PeriodicalIF":2.9,"publicationDate":"2025-03-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10918821","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143896493","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":"Hierarchical GraphCut Phase Unwrapping Based on Invariance of Diffeomorphisms Framework","authors":"Xiang Gao;Xinmu Wang;Zhou Zhao;Junqi Huang;Xianfeng David Gu","doi":"10.1109/OJSP.2025.3568757","DOIUrl":"https://doi.org/10.1109/OJSP.2025.3568757","url":null,"abstract":"Recent years have witnessed rapid advancements in 3D scanning technologies, with diverse applications spanning VR/AR, digital human creation, and medical imaging. Structured-light scanning with phase-shifting techniques is preferred for its use of non-radiative, low-intensity visible light and high accuracy, making it well suited for human-centric applications such as capturing 4D facial dynamics. A key step in these systems is phase unwrapping, which recovers continuous phase values from measurements that are inherently wrapped modulo <inline-formula><tex-math>$2pi$</tex-math></inline-formula>. The goal is to estimate the unwrapped phase count <inline-formula><tex-math>$k$</tex-math></inline-formula>, an integer-valued variable in the equation <inline-formula><tex-math>$Phi=phi + 2pi k$</tex-math></inline-formula>, where <inline-formula><tex-math>$phi$</tex-math></inline-formula> is the wrapped phase and <inline-formula><tex-math>$Phi$</tex-math></inline-formula> is the true phase. However, the presence of noise, occlusions, and piecewise continuous phase functions induced by complex 3D surface geometry makes the inverse reconstruction of the true phase extremely challenging. This is because phase unwrapping is an inherently ill-posed problem: measurements only provide modulo <inline-formula><tex-math>$2pi$</tex-math></inline-formula> values, and recovering the correct unwrapped phase count requires strong assumptions about the smoothness or continuity of the underlying 3D surface. Existing methods typically involve a trade-off between speed and accuracy: Fast approaches lack precision, while accurate algorithms are too slow for real-time use. To overcome these limitations, this work proposes a novel phase unwrapping framework that reformulates GraphCut-based unwrapping as a pixel-labeling problem. This framework helps significantly improve the estimation of the unwrapped phase count <inline-formula><tex-math>$k$</tex-math></inline-formula> through the invariance property of diffeomorphisms applied in image space via conformal and optimal transport (OT) maps. An odd number of diffeomorphisms are precomputed from the input phase data, and a hierarchical GraphCut algorithm is applied in each corresponding domain. The resulting label maps are fused via majority voting to efficiently and robustly estimate the unwrapped phase count <inline-formula><tex-math>$k$</tex-math></inline-formula> at each pixel, using an odd number of votes to break ties. Experimental results demonstrate a 45.5× speedup and lower <inline-formula><tex-math>$L^{2}$</tex-math></inline-formula> error in both real experiments and simulations, showing potential for real-time applications.","PeriodicalId":73300,"journal":{"name":"IEEE open journal of signal processing","volume":"6 ","pages":"546-554"},"PeriodicalIF":2.9,"publicationDate":"2025-03-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10994451","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144178954","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":"Multi-Channel Replay Speech Detection Using an Adaptive Learnable Beamformer","authors":"Michael Neri;Tuomas Virtanen","doi":"10.1109/OJSP.2025.3568758","DOIUrl":"https://doi.org/10.1109/OJSP.2025.3568758","url":null,"abstract":"Replay attacks belong to the class of severe threats against voice-controlled systems, exploiting the easy accessibility of speech signals by recorded and replayed speech to grant unauthorized access to sensitive data. In this work, we propose a multi-channel neural network architecture called M-ALRAD for the detection of replay attacks based on spatial audio features. This approach integrates a learnable adaptive beamformer with a convolutional recurrent neural network, allowing for joint optimization of spatial filtering and classification. Experiments have been carried out on the ReMASC dataset, which is a state-of-the-art multi-channel replay speech detection dataset encompassing four microphones with diverse array configurations and four environments. Results on the ReMASC dataset show the superiority of the approach compared to the state-of-the-art and yield substantial improvements for challenging acoustic environments. In addition, we demonstrate that our approach is able to better generalize to unseen environments with respect to prior studies.","PeriodicalId":73300,"journal":{"name":"IEEE open journal of signal processing","volume":"6 ","pages":"530-535"},"PeriodicalIF":2.9,"publicationDate":"2025-03-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10994395","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144117203","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":"P-TAME: Explain Any Image Classifier With Trained Perturbations","authors":"Mariano V. Ntrougkas;Vasileios Mezaris;Ioannis Patras","doi":"10.1109/OJSP.2025.3568756","DOIUrl":"https://doi.org/10.1109/OJSP.2025.3568756","url":null,"abstract":"The adoption of Deep Neural Networks (DNNs) in critical fields where predictions need to be accompanied by justifications is hindered by their inherent black-box nature. This paper introduces P-TAME (Perturbation-based Trainable Attention Mechanism for Explanations), a model-agnostic method for explaining DNN-based image classifiers. P-TAME employs an auxiliary image classifier to extract features from the input image, bypassing the need to tailor the explanation method to the internal architecture of the backbone classifier being explained. Unlike traditional perturbation-based methods, which have high computational requirements, P-TAME offers an efficient alternative by generating high-resolution explanations in a single forward pass during inference. We apply P-TAME to explain the decisions of VGG-16, ResNet-50, and ViT-B-16, three distinct and widely used image classifiers. Quantitative and qualitative results show that P-TAME matches or outperforms previous explainability methods, including model-specific ones.","PeriodicalId":73300,"journal":{"name":"IEEE open journal of signal processing","volume":"6 ","pages":"536-545"},"PeriodicalIF":2.9,"publicationDate":"2025-03-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10994422","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144171031","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}