IEEE Signal Processing Letters最新文献

Blind Capon Beamformer Based on Independent Component Extraction: Single-Parameter Algorithm

IF 3.2 2区工程技术

IEEE Signal Processing Letters Pub Date : 2025-02-13 DOI: 10.1109/LSP.2025.3538261

Zbyněk Koldovský;Jaroslav Čmejla;Stephen O'Regan

引用次数: 0

Look Twice and Closer: A Coarse-to-Fine Segmentation Network for Small Objects in Remote Sensing Images

IF 3.2 2区工程技术

IEEE Signal Processing Letters Pub Date : 2025-02-10 DOI: 10.1109/LSP.2025.3540374

Silin Chen;Qingzhong Wang;Kangjian Di;Haoyi Xiong;Ningmu Zou

{"title":"Look Twice and Closer: A Coarse-to-Fine Segmentation Network for Small Objects in Remote Sensing Images","authors":"Silin Chen;Qingzhong Wang;Kangjian Di;Haoyi Xiong;Ningmu Zou","doi":"10.1109/LSP.2025.3540374","DOIUrl":"https://doi.org/10.1109/LSP.2025.3540374","url":null,"abstract":"Convolutional neural networks (CNNs) are frequently used to analyze remote sensing images and achieve impressive progress. Limited by the receptive field size of CNNs, small objects tended to lack adequate features to obtain more accurate segmentation results. To address this problem, we introduce a novel CNN model for coarse-to-fine segmentation called C2FNet. C2FNet comprises two stages: the coarse network and the fine network. The coarse network identifies the positions and coarse segmentation outcomes of small objects in the input image. The fine network then takes a closer look at the small objects and re-segments the patches using binary segmentation. The fine network distinguishes small objects from the background to refine small object segmentation. Finally, C2FNet employs an aggregation module that merges the binary segmentation maps and coarse outcomes to obtain accurate small object segmentation. We conducted extensive experiments on three widely accepted datasets for remote sensing image segmentation, namely the ISPRS 2-D semantic labeling Potsdam, Vaihingen, and iSAID. Our approach significantly improves the performance of baseline models, achieving a 0.24%–2.83% increase in IoU per small object class on iSAID.","PeriodicalId":13154,"journal":{"name":"IEEE Signal Processing Letters","volume":"32 ","pages":"826-830"},"PeriodicalIF":3.2,"publicationDate":"2025-02-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143480781","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Deep Unrolled Graph Laplacian Regularization for Robust Time-of-Flight Depth Denoising

IF 3.2 2区工程技术

IEEE Signal Processing Letters Pub Date : 2025-02-07 DOI: 10.1109/LSP.2025.3539908

Jingwei Jia;Changyong He;Jianhui Wang;Gene Cheung;Jin Zeng

{"title":"Deep Unrolled Graph Laplacian Regularization for Robust Time-of-Flight Depth Denoising","authors":"Jingwei Jia;Changyong He;Jianhui Wang;Gene Cheung;Jin Zeng","doi":"10.1109/LSP.2025.3539908","DOIUrl":"https://doi.org/10.1109/LSP.2025.3539908","url":null,"abstract":"Depth images captured by Time-of-Flight (ToF) sensors are subject to severe noise. Recent approaches based on deep neural networks achieve good depth denoising performance in synthetic data, but the application to real-world data is limited, due to the complexity of actual depth noise characteristics and the difficulty in acquiring ground truth. In this paper, we propose a novel ToF depth denoising network based on unrolled graph Laplacian regularization to “robustify” the network against both noise complexity and dataset deficiency. Unlike previous schemes that are ignorant of underlying ToF imaging mechanism, we formulate a fidelity term in the optimization problem to adapt to the depth probabilistic distribution with spatially-varying noise variance. Then, we add quadratic graph Laplacian regularization as the smoothness prior, leading to a maximum a posteriori problem that is optimized efficiently by solving a linear system of equations. We unroll the solution into iterative filters so that parameters used in the optimization and graph construction are amendable to data-driven tuning. Because the resulting network is built using domain knowledge of ToF imaging principle and graph prior, it is robust against overfitting to synthetic training data. Experimental results demonstrate that the proposal outperforms existing schemes in ToF depth denoising on synthetic FLAT dataset and generalizes well to real Kinectv2 dataset.","PeriodicalId":13154,"journal":{"name":"IEEE Signal Processing Letters","volume":"32 ","pages":"821-825"},"PeriodicalIF":3.2,"publicationDate":"2025-02-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143480800","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Fitting Multiple Machine Learning Models With Performance Based Clustering

IF 3.2 2区工程技术

IEEE Signal Processing Letters Pub Date : 2025-02-03 DOI: 10.1109/LSP.2025.3537997

Mehmet E. Lorasdagi;Ahmet B. Koc;Ali T. Koc;Suleyman S. Kozat

引用次数: 0

An Adaptive CFAR Target Detector Based on the Quadratic Sum of Sample Autocovariances

IF 3.2 2区工程技术

IEEE Signal Processing Letters Pub Date : 2025-01-30 DOI: 10.1109/LSP.2025.3537329

Chang Qu;Jing Chen;Xiaoying Wang;Jiang Hu;Junping Yin

引用次数: 0

Robust Sequential Phase Estimation Using Multi-Temporal SAR Image Series

IF 3.2 2区工程技术

IEEE Signal Processing Letters Pub Date : 2025-01-30 DOI: 10.1109/LSP.2025.3537334

Dana El Hajjar;Guillaume Ginolhac;Yajing Yan;Mohammed Nabil El Korso

引用次数: 0

HiFiMSFA: Robust and High-Fidelity Image Watermarking Using Attention Augmented Deep Network

IF 3.2 2区工程技术

IEEE Signal Processing Letters Pub Date : 2025-01-28 DOI: 10.1109/LSP.2025.3535216

Yulin Zhang;Jiangqun Ni;Wenkang Su

{"title":"HiFiMSFA: Robust and High-Fidelity Image Watermarking Using Attention Augmented Deep Network","authors":"Yulin Zhang;Jiangqun Ni;Wenkang Su","doi":"10.1109/LSP.2025.3535216","DOIUrl":"https://doi.org/10.1109/LSP.2025.3535216","url":null,"abstract":"In recent years, the popularity of digital media sharing, especially high-quality images through online social networks (OSNs) has spurred an increasing demand for digital rights management (DRM) with watermarking. Although the most recent watermarking schemes with deep networks have exhibited considerable performance improvement, they still fall short in resisting multiple attacks with high-fidelity watermarking. To tackle this issue, a customized framework with encoder/decoder structure is proposed in this letter, aiming to consistently improve the robustness performance against multiple attacks. In specific, the <bold>Multi-scale <bold>Salient <bold>Feature <bold>Attention <bold>Block (MSFABlock) is exploited to effectively extract the robust image features with the encoder and decoder by taking advantage of the salient features, e.g., the image features obtained with difference of Gaussian (DoG) and other gradient operators. In addition, an adaptive squared Hinge function is developed as message loss to encourage adaptive watermark embedding. Experimental results demonstrate excellent performance in terms of robustness and perceptual fidelity as well as high efficiency of the proposed scheme in comparison to other SOTA methods.","PeriodicalId":13154,"journal":{"name":"IEEE Signal Processing Letters","volume":"32 ","pages":"781-785"},"PeriodicalIF":3.2,"publicationDate":"2025-01-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143446295","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Iterative Closest Point via MultiKernel Correntropy for Point Cloud Fine Registration

IF 3.2 2区工程技术

IEEE Signal Processing Letters Pub Date : 2025-01-27 DOI: 10.1109/LSP.2025.3535221

Hao Yi;Limei Hu;Feng Chen;Xiaoping Ren;Shukai Duan

引用次数: 0

TriMatch: Triple Matching for Text-to-Image Person Re-Identification

IF 3.2 2区工程技术

IEEE Signal Processing Letters Pub Date : 2025-01-27 DOI: 10.1109/LSP.2025.3534689

Shuanglin Yan;Neng Dong;Shuang Li;Huafeng Li

{"title":"TriMatch: Triple Matching for Text-to-Image Person Re-Identification","authors":"Shuanglin Yan;Neng Dong;Shuang Li;Huafeng Li","doi":"10.1109/LSP.2025.3534689","DOIUrl":"https://doi.org/10.1109/LSP.2025.3534689","url":null,"abstract":"Text-to-image person re-identification (TIReID) is a cross-modal retrieval task that aims to retrieve target person images based on a given text description. Existing methods primarily focus on mining the semantic associations across modalities, relying on the matching between heterogeneous features for retrieval. However, due to the inherent heterogeneous gaps between modalities, it is challenging to establish precise semantic associations, particularly in fine-grained correspondences, often leading to incorrect retrieval results. To address this issue, this letter proposes an innovative Triple Matching (TriMatch) framework that integrates cross-modal (image-text) matching and unimodal (image-image, text-text) matching for high-precision person retrieval. The framework introduces a generation task that performs cross-modal (image-to-text and text-to-image) feature generation and intra-modal feature alig achieve unimodal matching. By incorporating the generation task, TriMatch considers not only the semantic correlations between modalities but also the semantic consistency within single modalities, thereby effectively enhancing the accuracy of target person retrieval. Extensive experiments on multiple datasets demonstrate the superiority of TriMatch over existing methods.","PeriodicalId":13154,"journal":{"name":"IEEE Signal Processing Letters","volume":"32 ","pages":"806-810"},"PeriodicalIF":3.2,"publicationDate":"2025-01-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143480768","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Diffusion Generalized Minimum Total Error Entropy Algorithm

IF 3.2 2区工程技术

IEEE Signal Processing Letters Pub Date : 2025-01-24 DOI: 10.1109/LSP.2025.3533206

Peng Cai;Dongyuan Lin;Junhui Qian;Yunfei Zheng;Shiyuan Wang

引用次数: 0