IEEE Signal Processing Letters最新文献_第2页

Real-Time Self-Supervised Ultrasound Image Enhancement Using Test-Time Adaptation for Sophisticated Rotator Cuff Tear Diagnosis 使用测试时间适应的实时自监督超声图像增强用于复杂的肩袖撕裂诊断

IF 3.2 2区工程技术

IEEE Signal Processing Letters Pub Date : 2025-04-03 DOI: 10.1109/LSP.2025.3557754

Haeyun Lee;Kyungsu Lee;Jong Pil Yoon;Jihun Kim;Jun-Young Kim

{"title":"Real-Time Self-Supervised Ultrasound Image Enhancement Using Test-Time Adaptation for Sophisticated Rotator Cuff Tear Diagnosis","authors":"Haeyun Lee;Kyungsu Lee;Jong Pil Yoon;Jihun Kim;Jun-Young Kim","doi":"10.1109/LSP.2025.3557754","DOIUrl":"https://doi.org/10.1109/LSP.2025.3557754","url":null,"abstract":"Medical ultrasound imaging is a key diagnostic tool across various fields, with computer-aided diagnosis systems benefiting from advances in deep learning. However, its lower resolution and artifacts pose challenges, particularly for non-specialists. The simultaneous acquisition of degraded and high-quality images is infeasible, limiting supervised learning approaches. Additionally, self-supervised and zero-shot methods require extensive processing time, conflicting with the real-time demands of ultrasound imaging. Therefore, to address the aforementioned issues, we propose real-time ultrasound image enhancement via a self-supervised learning technique and a test-time adaptation for sophisticated rotational cuff tear diagnosis. The proposed approach learns from other domain image datasets and performs self-supervised learning on an ultrasound image during inference for enhancement. Our approach not only demonstrated superior ultrasound image enhancement performance compared to other state-of-the-art methods but also achieved an 18% improvement in the RCT segmentation performance.","PeriodicalId":13154,"journal":{"name":"IEEE Signal Processing Letters","volume":"32 ","pages":"1635-1639"},"PeriodicalIF":3.2,"publicationDate":"2025-04-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143865252","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

Bayesian Language Model Adaptation for Personalized Speech Recognition 个性化语音识别的贝叶斯语言模型自适应

IF 3.2 2区工程技术

IEEE Signal Processing Letters Pub Date : 2025-04-02 DOI: 10.1109/LSP.2025.3556787

Mun-Hak Lee;Ji-Hwan Mo;Ji-Hun Kang;Jin-Young Son;Joon-Hyuk Chang

引用次数: 0

Viewport-Independent Blind Quality Assessment of AI-Generated Omnidirectional Images via Vision-Language Correspondence 基于视觉语言对应的人工智能生成全方位图像的视口独立盲质评估

IF 3.2 2区工程技术

IEEE Signal Processing Letters Pub Date : 2025-04-02 DOI: 10.1109/LSP.2025.3556791

Xuelin Liu;Jiebin Yan;Chenyi Lai;Yang Li;Yuming Fang

{"title":"Viewport-Independent Blind Quality Assessment of AI-Generated Omnidirectional Images via Vision-Language Correspondence","authors":"Xuelin Liu;Jiebin Yan;Chenyi Lai;Yang Li;Yuming Fang","doi":"10.1109/LSP.2025.3556791","DOIUrl":"https://doi.org/10.1109/LSP.2025.3556791","url":null,"abstract":"The advancement of deep generation technology has significantly enhanced the growth of artificial intelligence-generated content (AIGC). Among these, AI-generated omnidirectional images (AGOIs), hold considerable promise for applications in virtual reality (VR). However, the quality of AGOIs varies widely, and there has been limited research focused on their quality assessment. In this letter, inspired by the characteristics of the human visual system, we propose a novel viewport-independent blindquality assessment method for AGOIs, termed VI-AGOIQA, which leverages vision-language correspondence. Specifically, to minimize the computational burden associated with viewport-based prediction methods for omnidirectional image quality assessment, a set of image patches are first extracted from AGOIs in Equirectangular Projection (ERP) format. Then, the correspondence between visual and textual inputs is effectively learned by utilizing the pre-trained image and text encoders of the Contrastive Language-Image Pre-training (CLIP) model. Finally, a multimodal feature fusion module is applied to predict human visual preferences based on the learned knowledge of visual-language consistency. Extensive experiments conducted on publicly available database demonstrate the promising performance of the proposed method.","PeriodicalId":13154,"journal":{"name":"IEEE Signal Processing Letters","volume":"32 ","pages":"1630-1634"},"PeriodicalIF":3.2,"publicationDate":"2025-04-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143865253","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

Multi-Party Reversible Data Hiding in Ciphertext Binary Images Based on Visual Cryptography 基于视觉密码学的密文二值图像多方可逆数据隐藏

IF 3.2 2区工程技术

IEEE Signal Processing Letters Pub Date : 2025-04-02 DOI: 10.1109/LSP.2025.3557273

Bing Chen;Jingkun Yu;Bingwen Feng;Wei Lu;Jun Cai

引用次数: 0

Perceptual Screen Content Image Hashing Using Adaptive Texture and Shape Features 使用自适应纹理和形状特征的感知屏幕内容图像哈希

IF 3.2 2区工程技术

IEEE Signal Processing Letters Pub Date : 2025-04-02 DOI: 10.1109/LSP.2025.3557272

Xue Yang;Ziqing Huang;Yonghua Zhang;Shuo Zhang;Zhenjun Tang

{"title":"Perceptual Screen Content Image Hashing Using Adaptive Texture and Shape Features","authors":"Xue Yang;Ziqing Huang;Yonghua Zhang;Shuo Zhang;Zhenjun Tang","doi":"10.1109/LSP.2025.3557272","DOIUrl":"https://doi.org/10.1109/LSP.2025.3557272","url":null,"abstract":"With the flourishing development of multi-client interactive systems, a new type of digital image known as Screen Content Image (SCI) has emerged. Unlike traditional natural scene images, SCI encompasses various visual contents, including natural images, graphics, and text. Because the multi-region distribution characteristics of screen content images result in the presence of blank regions, malicious modifications are easier to operate and harder to perceive, making a serious threat to visual content security. To this end, this paper proposes a color screen content image hashing algorithm using adaptive text regions features and global shape features. Specifically, the text regions are adaptively collected by calculating the local standard deviation of sub-blocks. Then, quaternion Fourier significant maps are computed for the text regions, and texture statistical features are further extracted to reflect the essential visual content robustness. Moreover, the global shape features are represented from the entire color SCI to ensure the discrimination. Finally, the hash sequence with a length of 142 bits is derived from the above features. Importantly, a specialized tampering dataset for SCIs has been established, and the proposed hashing shows highly sensitive to malicious modifications with a satisfactory detection accuracy. Meanwhile, the ROC curve analysis indicates that the proposed method outperforms existing hashing algorithms.","PeriodicalId":13154,"journal":{"name":"IEEE Signal Processing Letters","volume":"32 ","pages":"1655-1659"},"PeriodicalIF":3.2,"publicationDate":"2025-04-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143883431","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

MDFormer: Multi-Scale Downsampling-Based Transformer for Low-Light Image Enhancement MDFormer：基于多尺度下采样的弱光图像增强变压器

IF 3.2 2区工程技术

IEEE Signal Processing Letters Pub Date : 2025-04-01 DOI: 10.1109/LSP.2025.3556786

Yang Zhou;Liangtian He;Liang-Jian Deng;Hongming Chen;Chao Wang

{"title":"MDFormer: Multi-Scale Downsampling-Based Transformer for Low-Light Image Enhancement","authors":"Yang Zhou;Liangtian He;Liang-Jian Deng;Hongming Chen;Chao Wang","doi":"10.1109/LSP.2025.3556786","DOIUrl":"https://doi.org/10.1109/LSP.2025.3556786","url":null,"abstract":"Vision Transformers have achieved impressive performance in the field of low-light image enhancement. Some Transformer-based methods acquire attention maps within channel dimension, whereas the spatial resolutions of queries and keys involved in matrix multiplication are much larger than the dimensions of channels. During the key-query dot-product interaction to generate attention maps, massive information redundancy and expensive computational costs are incurred. Simultaneously, most previous feed-forward networks in Transformers do not model the multi-range information that plays an important role for feature reconstruction. Based on the above observations, we propose an effective Multi-Scale Downsampling-Based Transformer (MDFormer) for low-light image enhancement, which consists of multi-scale downsampling-based self-attention (MDSA) and multi-range gated extraction block (MGEB). MDSA employs downsampling with two different factors for queries and keys to save the computational cost when implementing self-attention operations within channel dimension. Furthermore, we introduce learnable parameters for the two generated attention maps to adjust the weights for fusion, which allows MDSA to adaptively retain the most significant attention scores from attention maps. The proposed MGEB captures multi-range information by virtue of the multi-scale depth-wise convolutions and dilated convolutions, to enhance modeling capabilities. Extensive experiments on four challenging low-light image enhancement datasets demonstrate that our method outperforms the state-of-the-art.","PeriodicalId":13154,"journal":{"name":"IEEE Signal Processing Letters","volume":"32 ","pages":"1575-1579"},"PeriodicalIF":3.2,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143856192","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 Self-Tuning SBL With Total Variation Priors for Block-Sparse Signal Recovery 块稀疏信号恢复的全变异先验自适应自调谐SBL

IF 3.2 2区工程技术

IEEE Signal Processing Letters Pub Date : 2025-04-01 DOI: 10.1109/LSP.2025.3556790

Hamza Djelouat;Reijo Leinonen;Mikko J. Sillanpää;Bhaskar D. Rao;Markku Juntti

引用次数: 0

CLIP-TNseg: A Multi-Modal Hybrid Framework for Thyroid Nodule Segmentation in Ultrasound Images CLIP-TNseg：超声图像中甲状腺结节分割的多模态混合框架

IF 3.2 2区工程技术

IEEE Signal Processing Letters Pub Date : 2025-04-01 DOI: 10.1109/LSP.2025.3556789

Xinjie Sun;Boxiong Wei;Yalong Jiang;Liquan Mao;Qi Zhao

引用次数: 0

TJCMNet: An Efficient Vision-Text Joint Identity Clues Mining Network for Visible-Infrared Person Re-Identification TJCMNet：一种高效的视觉-文本联合身份线索挖掘网络，用于可见-红外人物再识别

IF 3.2 2区工程技术

IEEE Signal Processing Letters Pub Date : 2025-04-01 DOI: 10.1109/LSP.2025.3556784

ZhuXuan Cheng;ZhiJia Zhang;Huijie Fan;XingQi Na

{"title":"TJCMNet: An Efficient Vision-Text Joint Identity Clues Mining Network for Visible-Infrared Person Re-Identification","authors":"ZhuXuan Cheng;ZhiJia Zhang;Huijie Fan;XingQi Na","doi":"10.1109/LSP.2025.3556784","DOIUrl":"https://doi.org/10.1109/LSP.2025.3556784","url":null,"abstract":"Retrieving images for Visible-Infrared Person Re-identification task is challenging, because of the huge modality discrepancy caused by the different imaging principle of RGB and infrared cameras. Existing approaches rely on seeking distinctive information within unified visual feature space, ignoring the stable identity information brought by textual description. To overcome these problems, this letter propose a novel Text-vision Joint Clue Mining (TJCM) network to aggregate vision and text features, then distill the joint knowledge for enhancing the modality-shared branch. Specifically, we first extract modality-shared and textual features using a parameter-shared vision encoder and a text encoder. Then, a text-vision co-refinement module is proposed to refine the implicit information within vision feature and text feature, then aggregate them into joint feature. Finally, introduce the heterogeneous distillation alignment loss provides enhancement for modality-shared feature through joint knowledge distillation at feature-level and logit-level. Our TJCMNet achieves significant improvements over the state-of-the-art methods on three mainstream datasets.","PeriodicalId":13154,"journal":{"name":"IEEE Signal Processing Letters","volume":"32 ","pages":"1615-1619"},"PeriodicalIF":3.2,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143871028","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

Feature Bank-Guided Reconstruction for Anomaly Detection 特征库引导下的异常检测重建

IF 3.2 2区工程技术

IEEE Signal Processing Letters Pub Date : 2025-03-31 DOI: 10.1109/LSP.2025.3555544

Sihan He;Tao Zhang;Wei Song;Hongbin Yu

引用次数: 0