Pub Date : 2025-06-05DOI: 10.1109/OJSP.2025.3577089
Taiki Uchiyama;Mariko Isogawa
Image inpainting is a technique designed to remove unwanted regions from images and restore them. This technique is expected to be applied in various applications, including image editing, virtual reality (VR), mixed reality (MR), and augmented reality (AR). Typically, the inpainting process is based on missing regions predefined by user-applied masks. However, the specified areas may not always be ideal for inpainting, and the quality of the inpainting results varies depending on the annotated masked region. Therefore, this paper addresses the task of generating masks that improve inpainting results. To this end, we proposed a method that utilized No-Reference Image Quality Assessment (NR-IQA), which can score image quality without a reference image, to generate masked regions that maximize inpainting quality.
{"title":"Mask Optimization for Image Inpainting Using No-Reference Image Quality Assessment","authors":"Taiki Uchiyama;Mariko Isogawa","doi":"10.1109/OJSP.2025.3577089","DOIUrl":"https://doi.org/10.1109/OJSP.2025.3577089","url":null,"abstract":"Image inpainting is a technique designed to remove unwanted regions from images and restore them. This technique is expected to be applied in various applications, including image editing, virtual reality (VR), mixed reality (MR), and augmented reality (AR). Typically, the inpainting process is based on missing regions predefined by user-applied masks. However, the specified areas may not always be ideal for inpainting, and the quality of the inpainting results varies depending on the annotated masked region. Therefore, this paper addresses the task of <bold>generating masks that improve inpainting results</b>. To this end, we proposed a method that utilized No-Reference Image Quality Assessment (NR-IQA), which can score image quality without a reference image, to generate masked regions that maximize inpainting quality.","PeriodicalId":73300,"journal":{"name":"IEEE open journal of signal processing","volume":"6 ","pages":"856-864"},"PeriodicalIF":2.7,"publicationDate":"2025-06-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11025170","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144725309","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}
Pub Date : 2025-04-28DOI: 10.1109/OJSP.2025.3564830
Daniel S. Nicolau;Lucas A. Thomaz;Luis M. N. Tavora;Sergio M. M. Faria
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.
{"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}
Pub Date : 2025-04-28DOI: 10.1109/OJSP.2025.3564817
Ahmet Bilican;M. Akın Yılmaz;A. Murat Tekalp
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.
{"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}
Pub Date : 2025-04-21DOI: 10.1109/OJSP.2025.3562797
Nicholas Mehlman;Shri Narayanan
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.
{"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}
Pub Date : 2025-04-07DOI: 10.1109/OJSP.2025.3558686
Costas A. Kokke;Mario Coutino;Richard Heusdens;Geert Leus
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.
{"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}
Pub Date : 2025-04-02DOI: 10.1109/OJSP.2025.3557332
Erik G. Larsson;Nicolò Michelusi
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.
{"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}
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.
{"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}
Semantic segmentation of remote sensing images is extensively used in crop cover and type analysis, and environmental monitoring. In the semantic segmentation of remote sensing images, owning to the specificity of remote sensing images, not only the local context is required, but also the global context information makes an important role in it. Inspired by the powerful global modelling capability of Swin Transformer, we propose the LSENet network, which follows the encoder-decoder architecture of the UNet network. In encoding phase, we propose spatial enhancement module (SEM), which helps Swin Transformer further enhance feature extraction by encoding spatial information. In decoding stage, we propose local enhancement module (LEM), which is embedded in the Swin Transformer to improve the Swin Transformer to assist the network to obtain more local semantic information so as to classify pixels more accurately, especially in the edge region, the adding of LEM enables to obtain smoother edges. The experimental results on the Vaihingen and Potsdam datasets demonstrate the effectiveness of our proposed method. Specifically, the mIoU metric is 78.58% on the Potsdam dataset, 72.59% on the Vaihingen dataset and 64.49% on the OpenEarthMap dataset.
{"title":"Swin Transformer With Spatial and Local Context Augmentation for Enhanced Semantic Segmentation of Remote Sensing Images","authors":"Rong-Xing Ding;Yi-Han Xu;Gang Yu;Wen Zhou;Ding Zhou","doi":"10.1109/OJSP.2025.3573202","DOIUrl":"https://doi.org/10.1109/OJSP.2025.3573202","url":null,"abstract":"Semantic segmentation of remote sensing images is extensively used in crop cover and type analysis, and environmental monitoring. In the semantic segmentation of remote sensing images, owning to the specificity of remote sensing images, not only the local context is required, but also the global context information makes an important role in it. Inspired by the powerful global modelling capability of Swin Transformer, we propose the LSENet network, which follows the encoder-decoder architecture of the UNet network. In encoding phase, we propose spatial enhancement module (SEM), which helps Swin Transformer further enhance feature extraction by encoding spatial information. In decoding stage, we propose local enhancement module (LEM), which is embedded in the Swin Transformer to improve the Swin Transformer to assist the network to obtain more local semantic information so as to classify pixels more accurately, especially in the edge region, the adding of LEM enables to obtain smoother edges. The experimental results on the Vaihingen and Potsdam datasets demonstrate the effectiveness of our proposed method. Specifically, the mIoU metric is 78.58% on the Potsdam dataset, 72.59% on the Vaihingen dataset and 64.49% on the OpenEarthMap dataset.","PeriodicalId":73300,"journal":{"name":"IEEE open journal of signal processing","volume":"6 ","pages":"608-620"},"PeriodicalIF":2.9,"publicationDate":"2025-03-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11011931","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144299229","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}
Pub Date : 2025-03-23DOI: 10.1109/OJSP.2025.3572759
Mazen Abdelfattah;Z. Jane Wang;Rabab Ward
Safe navigation of autonomous vehicles requires accurate and rapid understanding of their dynamic 3D environment. Scene flow estimation models this dynamic environment by predicting point motion between sequential point cloud scans, and is crucial for safe navigation. Existing state-of-the-art scene flow estimation methods, based on test-time optimization, achieve high accuracy but suffer from significant latency, limiting their applicability in real-time onboard systems. This latency stems from both the iterative test-time optimization process and the inherent delay of waiting for the LiDAR to acquire a complete $360^circ$ scan. To overcome this bottleneck, we introduce a novel streaming scene flow framework leveraging the sequential nature of LiDAR slice acquisition, demonstrating a dramatic reduction in end-to-end latency. Instead of waiting for the full $360^circ$ scan, our method immediately estimates scene flow using each LiDAR slice once it is captured. To mitigate the reduced context of individual slices, we propose a novel contextual augmentation technique that expands the target slice by a small angular margin, incorporating crucial slice boundary information. Furthermore, to enhance test-time optimization within our streaming framework, our novel initialization scheme ’warm-starts' the current optimization using optimized parameters from the preceding slice. This achieves substantial speedups while maintaining, and in some cases surpassing, full-scan accuracy. We rigorously evaluate our approach on the challenging Waymo and Argoverse datasets, demonstrating significant latency reduction without compromising scene flow quality. This work paves the way for deploying high-accuracy, real-time scene flow algorithms in autonomous driving, advancing the field towards more responsive and safer autonomous systems.
{"title":"Streaming LiDAR Scene Flow Estimation","authors":"Mazen Abdelfattah;Z. Jane Wang;Rabab Ward","doi":"10.1109/OJSP.2025.3572759","DOIUrl":"https://doi.org/10.1109/OJSP.2025.3572759","url":null,"abstract":"Safe navigation of autonomous vehicles requires accurate and rapid understanding of their dynamic 3D environment. Scene flow estimation models this dynamic environment by predicting point motion between sequential point cloud scans, and is crucial for safe navigation. Existing state-of-the-art scene flow estimation methods, based on test-time optimization, achieve high accuracy but suffer from significant latency, limiting their applicability in real-time onboard systems. This latency stems from both the iterative test-time optimization process and the inherent delay of waiting for the LiDAR to acquire a complete <inline-formula><tex-math>$360^circ$</tex-math></inline-formula> scan. To overcome this bottleneck, we introduce a novel <italic>streaming</i> scene flow framework leveraging the sequential nature of LiDAR slice acquisition, demonstrating a dramatic reduction in end-to-end latency. Instead of waiting for the full <inline-formula><tex-math>$360^circ$</tex-math></inline-formula> scan, our method immediately estimates scene flow using each LiDAR slice once it is captured. To mitigate the reduced context of individual slices, we propose a novel contextual augmentation technique that expands the target slice by a small angular margin, incorporating crucial slice boundary information. Furthermore, to enhance test-time optimization within our streaming framework, our novel initialization scheme ’warm-starts' the current optimization using optimized parameters from the preceding slice. This achieves substantial speedups while maintaining, and in some cases surpassing, full-scan accuracy. We rigorously evaluate our approach on the challenging Waymo and Argoverse datasets, demonstrating significant latency reduction without compromising scene flow quality. This work paves the way for deploying high-accuracy, real-time scene flow algorithms in autonomous driving, advancing the field towards more responsive and safer autonomous systems.","PeriodicalId":73300,"journal":{"name":"IEEE open journal of signal processing","volume":"6 ","pages":"590-598"},"PeriodicalIF":2.9,"publicationDate":"2025-03-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11012710","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144243669","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}
Pub Date : 2025-03-23DOI: 10.1109/OJSP.2025.3572820
Jeova Farias Sales Rocha Neto
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.
{"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}
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