IEEE open journal of signal processing最新文献

{"title":"Appearance Estimation and Image Segmentation via Tensor Factorization","authors":"Jeova Farias Sales Rocha Neto","doi":"10.1109/OJSP.2025.3572820","DOIUrl":"https://doi.org/10.1109/OJSP.2025.3572820","url":null,"abstract":"Image Segmentation is one of the core tasks in Computer Vision, and solving it often depends on modeling the image appearance data via the color distributions of each of its constituent regions. Whereas many segmentation algorithms handle the appearance model dependence using alternation or implicit methods, we propose here a new approach to directly estimate them from the image without prior information on the underlying segmentation. Our method uses local high-order color statistics from the image as an input to a tensor factorization-based estimator for latent variable models. This approach is able to estimate models in multi-region images and automatically output the regions' proportions without prior user interaction, overcoming the drawbacks of a prior attempt to this problem. We also demonstrate the performance of our proposed method in many challenging synthetic and real imaging scenarios and show that it leads to an efficient segmentation algorithm.","PeriodicalId":73300,"journal":{"name":"IEEE open journal of signal processing","volume":"6 ","pages":"581-589"},"PeriodicalIF":2.9,"publicationDate":"2025-03-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11011680","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144243738","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":"Snapshot Hyperspectral Imaging With Co-Designed Optics, Color Filter Array, and Unrolled Network","authors":"Ayoung Kim;Ugur Akpinar;Erdem Sahin;Atanas Gotchev","doi":"10.1109/OJSP.2025.3571675","DOIUrl":"https://doi.org/10.1109/OJSP.2025.3571675","url":null,"abstract":"We propose a novel snapshot hyperspectral imaging method that incorporates co-designed optics, a color filter array (CFA), and an unrolled post-processing network through end-to-end learning. The camera optics consists of a fixed refractive lens and a diffractive optical element (DOE). The learned DOE and CFA efficiently encode the hyperspectral data cube on the sensor via phase and amplitude modulation at the camera aperture and sensor planes, respectively. Subsequently, the unrolled network reconstructs the hyperspectral images from the sensor signal with high accuracy. We conduct extensive simulations to analyze and validate the performance of the proposed method for several CFA models and in non-ideal imaging conditions. We demonstrate that the Gaussian model is effective for parameterizing the spectral transmission functions of CFA pixels, providing high reconstruction accuracy and being relatively easy to implement. Furthermore, we show that learned CFA patterns are effective when optimally coupled with co-designed diffractive-refractive optics. We evaluate the robustness of our method against sensor noise and potential inaccuracies in the fabrication of the DOE and CFA. Our results show that our method achieves superior reconstruction quality compared to state-of-the-art methods, excelling in both spatial and spectral detail recovery and maintaining robustness against realistic noise levels.","PeriodicalId":73300,"journal":{"name":"IEEE open journal of signal processing","volume":"6 ","pages":"599-607"},"PeriodicalIF":2.9,"publicationDate":"2025-03-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11008739","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144243739","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":"WaViT-CDC: Wavelet Vision Transformer With Central Difference Convolutions for Spatial-Frequency Deepfake Detection","authors":"Nour Eldin Alaa Badr;Jean-Christophe Nebel;Darrel Greenhill;Xing Liang","doi":"10.1109/OJSP.2025.3571679","DOIUrl":"https://doi.org/10.1109/OJSP.2025.3571679","url":null,"abstract":"The increasing popularity of generative AI has led to a significant rise in deepfake content, creating an urgent need for generalized and reliable deepfake detection methods. Since existing approaches rely on either spatial-domain features or frequency-domain features, they struggle to generalize across unseen datasets, especially those with subtle manipulations. To address these challenges, a novel end-to-end Wavelet Central Difference Convolutional Vision Transformer framework is designed to enhance spatial-frequency deepfake detection. Unlike previous methods, this approach applies the Discrete Wavelet Transform for multi-level frequency decomposition and Central Difference Convolution to capture local fine-grained discrepancies and focus on texture variances, while also incorporating Vision Transformers for global contextual understanding. The Frequency-Spatial Feature Fusion Attention module integrates these features, enabling the effective detection of fake artifacts. Moreover, in contrast to earlier work, subtle perturbations to both spatial and frequency domains are introduced to further improve generalization. Generalization cross-dataset evaluations demonstrate that WaViT-CDC outperforms state-of-the-art methods, when trained on both low-quality and high-quality face images, achieving an average performance increase of 2.5% and 4.5% on challenging high-resolution, real-world datasets such as Celeb-DF and WildDeepfake.","PeriodicalId":73300,"journal":{"name":"IEEE open journal of signal processing","volume":"6 ","pages":"621-630"},"PeriodicalIF":2.9,"publicationDate":"2025-03-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11007485","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144299230","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 Multi-Level Patch Dataset for JPEG Image Quality Assessment by Absolute Binary Decision","authors":"Soichiro Honda;Yoshihiro Maeda;Osamu Watanabe;Norishige Fukushima","doi":"10.1109/OJSP.2025.3571674","DOIUrl":"https://doi.org/10.1109/OJSP.2025.3571674","url":null,"abstract":"Image quality assessment (IQA) plays a fundamental role in evaluating image processing. Currently, JPEG AIC specifies the IQA methods, dividing them into three levels: AIC-1, 2, and 3. AIC-1 measures the quality from low to high, AIC-2 focuses on the threshold for visual losslessness, and AIC-3 measures the range between 1 and 2. AIC-3 requires complex processing and many comparisons, such as using boosted triplets to obtain highly accurate JNDs and then using those JNDs to create scale scores, or generating many combinations of triplets. In this study, we revisit the definition and propose a method for measuring the target band of AIC-3 by mixing the measurement methods of AIC-1 and AIC-2 and adjusting the sensitivity. This method presents the pristine and degraded images and asks whether they are the same or not. We called this absolute binary decision (ABD), referring to ACR in AIC-1. We constructed a JPEG-specific IQA dataset using ABD from distorted images that were progressively patched to relate the patches to the IQA of the entire images. As this was a new experiment, it was first conducted under laboratory control to ensure reliability. The experimental results showed that ABD could measure the QP40-90 range. In addition, it was found that patching differs from the entire image case. While patching draws attention to places that people do not usually pay attention to, usual image presentation concentrates attention through semantic guidance, suggesting the possibility that pseudo-attention patching is being performed on characteristic locations.","PeriodicalId":73300,"journal":{"name":"IEEE open journal of signal processing","volume":"6 ","pages":"631-640"},"PeriodicalIF":2.9,"publicationDate":"2025-03-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11007521","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144367073","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":"AFD: Defending Convolutional Neural Networks Without Using Adversarial Samples","authors":"Nupur Thakur;Yuzhen Ding;Baoxin Li","doi":"10.1109/OJSP.2025.3571681","DOIUrl":"https://doi.org/10.1109/OJSP.2025.3571681","url":null,"abstract":"The vulnerability of deep neural networks to adversarial attacks has attracted much research effort. Still, studies have shown that it is challenging to simultaneously achieve both strong robustness to adversarial attacks and low degradation in the performance on the original task, as there is always a trade-off between the two objectives. In this paper, we present a novel training strategy named Adversarial-Free Defense (AFD), which introduces a minimal change to a network architecture (by modifying the first convolution layer) while employing a learning algorithm that leads to special properties of the first-layer kernels. We show how this learning strategy enhances the robustness of the network to adversarial attacks (without using adversarial samples) while maintaining a reasonable performance on the original task. Empirical results including analysis in terms of the effective Lipschitz constant of the learned network suggest that, compared to most existing methods that rely on elaborate regularization schemes imposed on all layers, our seemingly simplistic approach demonstrates high effectiveness.","PeriodicalId":73300,"journal":{"name":"IEEE open journal of signal processing","volume":"6 ","pages":"571-580"},"PeriodicalIF":2.9,"publicationDate":"2025-03-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11007011","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144243740","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":"On the Design of Near-Optimal Generalized Block-Based Spatial Modulation With Low Detection Complexity","authors":"Yen-Ming Chen;Wei-Lun Lin;Heng Lee;Tsung-Lin Chen","doi":"10.1109/OJSP.2025.3568675","DOIUrl":"https://doi.org/10.1109/OJSP.2025.3568675","url":null,"abstract":"Spatial Modulation (SM) and Generalized Spatial Modulation (GSM) have attracted significant attention in the development of spectrally and energy-efficient transmission schemes for multiple-input multiple-output (MIMO) systems. However, independently designing the constellation cardinality and TACs leads to limited performance gains and an exponential increase in complexity, particularly under maximum-likelihood (ML) detection. To address these limitations, the generalized block-based spatial modulation (GBSM) scheme was proposed, enabling greater flexibility by jointly designing GSM signals across a block of time indices. Building on this idea, this paper first proposes a near-optimal codebook search method based on three-dimensional (3-D) mapping, applicable to both fast and slow Rayleigh fading channels. Secondly, a codebook-assisted tree-search detector (CATSD) is introduced, offering a 98% reduction in complexity compared to ML detection while maintaining near-ML error performance. Finally, an alternative codebook search method is presented, accompanied by a complexity analysis that reveals a favorable trade-off between performance and computational cost.","PeriodicalId":73300,"journal":{"name":"IEEE open journal of signal processing","volume":"6 ","pages":"555-570"},"PeriodicalIF":2.9,"publicationDate":"2025-03-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10994453","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144243737","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}