IEEE Signal Processing Letters最新文献

Dual-Branch Network for No-Reference Super-Resolution Image Quality Assessment

IF 3.2 2区工程技术

IEEE Signal Processing Letters Pub Date : 2025-03-21 DOI: 10.1109/LSP.2025.3553432

Tong Tang;Fan Yang;Xinyu Lin;Weisheng Li

引用次数: 0

DNGG: Medical Image Lossless Encryption via Deep Network Guided Generative

IF 3.2 2区工程技术

IEEE Signal Processing Letters Pub Date : 2025-03-18 DOI: 10.1109/LSP.2025.3552528

Lin Fan;Meng Li;Zhenting Hu;Yuan Hong;Dexing Kong

引用次数: 0

Optimizing the Order of Modes in Tensor Train Decomposition

IF 3.2 2区工程技术

IEEE Signal Processing Letters Pub Date : 2025-03-17 DOI: 10.1109/LSP.2025.3552005

Petr Tichavský;Ondřej Straka

引用次数: 0

A Two-Level Weighted Low-Complexity Adaptive Beamforming Method

IF 3.2 2区工程技术

IEEE Signal Processing Letters Pub Date : 2025-03-17 DOI: 10.1109/LSP.2025.3551957

Yuxi Du;Weijia Cui;Bin Ba;Yangyang Chen;Long Zhang

引用次数: 0

DCE-Net: A Dual-Frequency Domain Knowledge-Guided Framework for Image Dehazing via Detail and Content Enhancements

IF 3.2 2区工程技术

IEEE Signal Processing Letters Pub Date : 2025-03-14 DOI: 10.1109/LSP.2025.3551201

Jianlei Liu;Yuting Pang;Shilong Wang

{"title":"DCE-Net: A Dual-Frequency Domain Knowledge-Guided Framework for Image Dehazing via Detail and Content Enhancements","authors":"Jianlei Liu;Yuting Pang;Shilong Wang","doi":"10.1109/LSP.2025.3551201","DOIUrl":"https://doi.org/10.1109/LSP.2025.3551201","url":null,"abstract":"Existing image dehazing methods are largely constrained to spatial domain processing, failing to fully leverage the rich knowledge embedded in the frequency domain of clear images. Additionally, the traditional convolutional operations in network architectures limit their mapping capabilities to some extent. To address these issues, a novel image dehazing network, termed the Detail and Content Enhancement Network (DCE-Net), is proposed. DCE-Net redefines dehazing task from a frequency-domain perspective, incorporating differential convolution and attention mechanisms to design the High-Frequency Detail Enhancement Module (HDEM) and the Low-Frequency Content Enhancement Module (LCEM). Furthermore, a Dual-Frequency Domain Knowledge-Guided Strategy (DDKS) is introduced during the training phase to exploit the abundant frequency-domain priors inherent in clear images. Experimental results demonstrate that the DCE-Net achieves outstanding performance on both synthetic benchmark datasets and real-world hazy scenes. DCE-Net not only significantly restores image clarity and contrast but also effectively preserves details and content features.","PeriodicalId":13154,"journal":{"name":"IEEE Signal Processing Letters","volume":"32 ","pages":"1356-1360"},"PeriodicalIF":3.2,"publicationDate":"2025-03-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143725107","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

Enhanced Attention Context Model for Learned Image Compression

IF 3.2 2区工程技术

IEEE Signal Processing Letters Pub Date : 2025-03-14 DOI: 10.1109/LSP.2025.3551659

Zhengxin Chen;Xiaohai He;Chao Ren;Tingrong Zhang;Shuhua Xiong

{"title":"Enhanced Attention Context Model for Learned Image Compression","authors":"Zhengxin Chen;Xiaohai He;Chao Ren;Tingrong Zhang;Shuhua Xiong","doi":"10.1109/LSP.2025.3551659","DOIUrl":"https://doi.org/10.1109/LSP.2025.3551659","url":null,"abstract":"Recently, deep learning has witnessed encouraging advances in image compression. An accurate entropy model, which estimates the probability distribution of the latent representation and reduces the bits required for compressing an image, is one of the keys to the success of learned image compression methods. The latent representation presents potential correlations in local, non-local, and cross-channel contexts. However, most entropy models only consider partial correlations, leading to suboptimal entropy estimation. In this letter, we propose a novel enhanced attention context model (EACM) to make full use of various correlations between latent elements for accurate entropy estimation. The proposed EACM contains a local spatial attention block (LSAB), a local channel attention block (LCAB), a global spatial attention block (GSAB), and a global channel attention block (GCAB). LSAB, LCAB, GSAB, and GCAB are carefully designed to adaptively exploit local spatial, local channel, global spatial, and global channel correlations, respectively. The experimental results on benchmark datasets show that our image compression model with the proposed EACM outperforms several state-of-the-art methods quantitatively and qualitatively.","PeriodicalId":13154,"journal":{"name":"IEEE Signal Processing Letters","volume":"32 ","pages":"1301-1305"},"PeriodicalIF":3.2,"publicationDate":"2025-03-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143716527","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

XSNet: A Lightweight X-Ray Security Image Segmentation Model Combining State-Space Models and Convolutional Neural Networks

IF 3.2 2区工程技术

IEEE Signal Processing Letters Pub Date : 2025-03-14 DOI: 10.1109/LSP.2025.3550769

Weichao Jia;Wei Liu;Changsheng Zhang;Jian Fu;Qiong Liu

引用次数: 0

Algebraic Solution for Linear Array-Based 3D Localization Without Deployment Limitations

IF 3.2 2区工程技术

IEEE Signal Processing Letters Pub Date : 2025-03-14 DOI: 10.1109/LSP.2025.3551200

Chengyu Li;Beichuan Tang;Yanbing Yang;Liangyin Chen;Yimao Sun

引用次数: 0

A Distributed Penalty-Like Function Approach for the Nonconvex Constrained Optimization Problem

IF 3.2 2区工程技术

IEEE Signal Processing Letters Pub Date : 2025-03-13 DOI: 10.1109/LSP.2025.3551202

Xiasheng Shi;Darong Huang;Changyin Sun

引用次数: 0

Enhanced Swin Transformer and Edge Spatial Attention for Remote Sensing Image Semantic Segmentation

IF 3.2 2区工程技术

IEEE Signal Processing Letters Pub Date : 2025-03-12 DOI: 10.1109/LSP.2025.3550858

Fuxiang Liu;Zhiqiang Hu;Lei Li;Hanlu Li;Xinxin Liu

{"title":"Enhanced Swin Transformer and Edge Spatial Attention for Remote Sensing Image Semantic Segmentation","authors":"Fuxiang Liu;Zhiqiang Hu;Lei Li;Hanlu Li;Xinxin Liu","doi":"10.1109/LSP.2025.3550858","DOIUrl":"https://doi.org/10.1109/LSP.2025.3550858","url":null,"abstract":"Combining convolutional neural networks (CNNs) and transformers is a crucial direction in remote sensing image semantic segmentation. However, due to differences in the spatial information focus and feature extraction methods, existing feature transfer and fusion strategies do not effectively integrate the advantages of both approaches. To address these issues, we propose a CNN-transformer hybrid network for precise remote sensing image semantic segmentation. We propose a novel Swin Transformer block to optimize feature extraction and enable the model to handle remote sensing images of arbitrary sizes. Additionally, we design an Edge Spatial Attention module to focus attention on local edge structures, effectively integrating global features and local details. This facilitates efficient information flow between the Transformer encoder and CNN decoder. Finally, a multi-scale convolutional decoder is employed to fully leverage both global information from the Transformer and local features from the CNN, leading to accurate segmentation results. Our network achieved state-of-the-art performance on the Vaihingen and Potsdam datasets, reaching mIoU and F1 scores of 67.37% and 79.82%, as well as 72.39% and 83.68%, respectively.","PeriodicalId":13154,"journal":{"name":"IEEE Signal Processing Letters","volume":"32 ","pages":"1296-1300"},"PeriodicalIF":3.2,"publicationDate":"2025-03-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143716509","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