IEEE Signal Processing Letters最新文献_第6页

MIMO Radar Joint Heart Rate and Respiratory Rate Estimation and Performance Bound Analysis

IF 3.2 2区工程技术

IEEE Signal Processing Letters Pub Date : 2025-03-03 DOI: 10.1109/LSP.2025.3546894

Peichao Wang;Qian He;Haozheng Li

引用次数: 0

GLNet: Global-Local Fusion Network for Strip Steel Surface Defects Detection

IF 3.2 2区工程技术

IEEE Signal Processing Letters Pub Date : 2025-03-03 DOI: 10.1109/LSP.2025.3546888

Xuan Wang;Liuxin Bao;Xiaofei Zhou;Lei Xia;Xiaobin Xu

{"title":"GLNet: Global-Local Fusion Network for Strip Steel Surface Defects Detection","authors":"Xuan Wang;Liuxin Bao;Xiaofei Zhou;Lei Xia;Xiaobin Xu","doi":"10.1109/LSP.2025.3546888","DOIUrl":"https://doi.org/10.1109/LSP.2025.3546888","url":null,"abstract":"Surface defect detection in strip steel is a critical task in industrial quality control. However, existing methods struggle with capturing both local details and global context effectively. In this paper, we propose the Global-Local Fusion Network (GLNet) for strip steel surface defect detection, which combines the advantages of VMamba's global feature extraction and CNN's local feature modeling. GLNet employs an encoder-decoder structure, where the encoder consists of two parallel branches: one based on VMamba for capturing global features and the other using ResNet50 for extracting local features. In the decoder, a Global-Local Fusion (GLF) module integrates these features using the Cross Prototype Objective Enhancement (CPOE) and Selective Spatial and Channel Attention (SSCA) modules. The CPOE module facilitates the interaction and fusion between global and local features, while the SSCA module digs the multi-scale information from the global feature through dynamic attention to guide the feature aggregation. Extensive experiments on the ESDIs dataset, demonstrate that GLNet achieves state-of-the-art performance in defect detection, surpassing 13 existing methods in both quantitative and qualitative metrics.","PeriodicalId":13154,"journal":{"name":"IEEE Signal Processing Letters","volume":"32 ","pages":"1256-1260"},"PeriodicalIF":3.2,"publicationDate":"2025-03-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143716529","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

Depth-Aided Color Image Inpainting in Quaternion Domain

IF 3.2 2区工程技术

IEEE Signal Processing Letters Pub Date : 2025-03-03 DOI: 10.1109/LSP.2025.3547662

Shunki Tatsumi;Ryo Hayakawa;Youji Iiguni

引用次数: 0

Zero-Shot Low-Light Image Enhancement via Joint Frequency Domain Priors Guided Diffusion

IF 3.2 2区工程技术

IEEE Signal Processing Letters Pub Date : 2025-03-03 DOI: 10.1109/LSP.2025.3547269

Jinhong He;Shivakumara Palaiahnakote;Aoxiang Ning;Minglong Xue

{"title":"Zero-Shot Low-Light Image Enhancement via Joint Frequency Domain Priors Guided Diffusion","authors":"Jinhong He;Shivakumara Palaiahnakote;Aoxiang Ning;Minglong Xue","doi":"10.1109/LSP.2025.3547269","DOIUrl":"https://doi.org/10.1109/LSP.2025.3547269","url":null,"abstract":"Due to the singularity of real-world paired datasets and the complexity of low-light environments, this leads to supervised methods lacking a degree of scene generalisation. Meanwhile, limited by poor lighting and content guidance, existing zero-shot methods cannot handle unknown severe degradation well. To address this problem, we will propose a new zero-shot low-light enhancement method to compensate for the lack of light and structural information in the diffusion sampling process by effectively combining the wavelet and Fourier frequency domains to construct rich a priori information. The key to the inspiration comes from the similarity between the wavelet and Fourier frequency domains: both light and structure information are closely related to specific frequency domain regions, respectively. Therefore, by transferring the diffusion process to the wavelet low-frequency domain and combining the wavelet and Fourier frequency domains by continuously decomposing them in the inverse process, the constructed rich illumination prior is utilised to guide the image generation enhancement process. Sufficient experiments show that the framework is robust and effective in various scenarios.","PeriodicalId":13154,"journal":{"name":"IEEE Signal Processing Letters","volume":"32 ","pages":"1091-1095"},"PeriodicalIF":3.2,"publicationDate":"2025-03-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143629597","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

Binary Neural Networks With Feature Information Retention for Efficient Image Classification

IF 3.2 2区工程技术

IEEE Signal Processing Letters Pub Date : 2025-03-03 DOI: 10.1109/LSP.2025.3546895

Rui Ding;Yuxiao Wang;Haijun Liu;Xichuan Zhou

{"title":"Binary Neural Networks With Feature Information Retention for Efficient Image Classification","authors":"Rui Ding;Yuxiao Wang;Haijun Liu;Xichuan Zhou","doi":"10.1109/LSP.2025.3546895","DOIUrl":"https://doi.org/10.1109/LSP.2025.3546895","url":null,"abstract":"Although binary neural networks (BNNs) enjoy extreme compression ratios, there are significant accuracy gap compared with full-precision models. Previous works propose various strategies to reduce the information loss induced by the binarization process, improving the performance of binary neural networks to some extent. However, in this letter, we argue that few studies try to alleviate this problem from the structure perspective, resulting in inferior performance. To this end, we propose a novel Feature Information Retention Network named FIRNet, which incorporates an extra path to propagate the untouched informative feature maps. Specifically, the FIRNet splits the input feature maps into two groups, one of which is fed into the normal layers and another kept untouched for information retention. Then we utilize the concatenation, shuffle and pooling operations to process these features with 64× memory saving. Finally, with only a 1.7% complexity increase, a FIR fusion layer is proposed to aggregate the features from two branches. Experimental results demonstrate that our proposed method achieves 1.0% Top-1 accuracy improvement over the baseline model and outperforms other state-of-the-art BNNs on the ImageNet dataset.","PeriodicalId":13154,"journal":{"name":"IEEE Signal Processing Letters","volume":"32 ","pages":"1321-1325"},"PeriodicalIF":3.2,"publicationDate":"2025-03-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143716523","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

Adaptive and Background-Aware Match for Class-Agnostic Counting

IF 3.2 2区工程技术

IEEE Signal Processing Letters Pub Date : 2025-03-03 DOI: 10.1109/LSP.2025.3546891

Shenjian Gong;Jian Yang;Shanshan Zhang

{"title":"Adaptive and Background-Aware Match for Class-Agnostic Counting","authors":"Shenjian Gong;Jian Yang;Shanshan Zhang","doi":"10.1109/LSP.2025.3546891","DOIUrl":"https://doi.org/10.1109/LSP.2025.3546891","url":null,"abstract":"Class-Agnostic Counting (CAC) aims to count object instances in an image by simply specifying a few exemplar boxes of interest. The key challenge for CAC is how to tailor a desirable interaction between exemplar and query features. Previous CAC methods implement such interaction by solely leveraging standard global feature convolution. We find this interaction leads to under-match caused by intra-class diversity and over-match on background, which harms counting performance severely. In this work, we propose a novel feature interaction method called Adaptive and Background-aware Match (ABM) against high intra-class diversity and noisy background. Concretely, given exemplar and query features, we improve the original high-dimensional coupled spaces match to Adaptive Orthogonal subspaces Match (AOM), avoiding under-match caused by intra-class diversity. Moreover, Background-Specific Match (BSM) employs interaction between the learnable background prototype and query features to provide global background priors, making the match be aware of background regions. Additionally, we find the high scale variance among different query images leads to bad counting performance for extremely small scale objects. Object-Scale Unify (OSU) is proposed to take the size of the exemplars as the scale prior and resize query images so that all objects are at a uniform average scale. Extensive experiments on FSC-147 show that our method performs better. We also conduct extensive ablation studies to demonstrate the effectiveness of each component of our proposed method.","PeriodicalId":13154,"journal":{"name":"IEEE Signal Processing Letters","volume":"32 ","pages":"1261-1265"},"PeriodicalIF":3.2,"publicationDate":"2025-03-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143716526","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

MSTDF: Motion Style Transfer Towards High Visual Fidelity Based on Dynamic Fusion

IF 3.2 2区工程技术

IEEE Signal Processing Letters Pub Date : 2025-03-03 DOI: 10.1109/LSP.2025.3546885

Ziyun Qian;Dingkang Yang;Mingcheng Li;Zeyu Xiao;Lihua Zhang

引用次数: 0

Low-Resource Speech Recognition of Radiotelephony Communications Based on Continuous Learning of In-Domain and Out-of-Domain Knowledge

IF 3.2 2区工程技术

IEEE Signal Processing Letters Pub Date : 2025-02-26 DOI: 10.1109/LSP.2025.3545955

Guimin Jia;Dong He;Xilong Zhou

引用次数: 0

A Burn After Reading Data Hiding Framework

IF 3.2 2区工程技术

IEEE Signal Processing Letters Pub Date : 2025-02-25 DOI: 10.1109/LSP.2025.3545805

Jingyuan Jiang;Zichi Wang;Guanghui He;Xinpeng Zhang

引用次数: 0

Hardware-Decoder-Friendly High Throughput String Prediction for SCC Implemented in AVS3

IF 3.2 2区工程技术

IEEE Signal Processing Letters Pub Date : 2025-02-25 DOI: 10.1109/LSP.2025.3545284

Liping Zhao;Zhuge Yan;Zongda Wu;Jiangda Wang;Tao Lin

引用次数: 0