IEEE Transactions on Image Processing最新文献

筛选
英文 中文
Enhancing Feature Learning with Hard Samples in Mutual Learning for Online Class Incremental Learning. 在线课堂增量学习中互学硬样本增强特征学习
IF 10.6 1区 计算机科学
IEEE Transactions on Image Processing Pub Date : 2025-10-08 DOI: 10.1109/tip.2025.3616626
Guoqiang Liang,Shibin Su,De Cheng,Shizhou Zhang,Peng Wang,Yanning Zhang
{"title":"Enhancing Feature Learning with Hard Samples in Mutual Learning for Online Class Incremental Learning.","authors":"Guoqiang Liang,Shibin Su,De Cheng,Shizhou Zhang,Peng Wang,Yanning Zhang","doi":"10.1109/tip.2025.3616626","DOIUrl":"https://doi.org/10.1109/tip.2025.3616626","url":null,"abstract":"Online Class-Incremental Learning (OCIL) aims to solve the problem of incrementally learning new classes from a non-i.i.d. and single-pass data stream. Compared to the offline setting, OCIL is much closer to a live learning experience requiring higher model update frequency at less computational budget. Due to its one-epoch training constraint, the model is likely to learn non-essential features and encounter the under-fitting issue, which severely affects the model's stability. In this paper, we investigate how to use hard samples to improve data variability, eventually enhancing feature learning and addressing the under-fitting problem. Specifically, by introducing a scoring function assessing the sample value, we build an OCIL formulation that simultaneously generates high-value samples and optimizes the OCIL model, improving generalization ability within the constraint of single-epoch training. Empirically, we found that strong data augmentation is a simple but effective way to generate a higher proportion of high-score samples. To make the most of these augmented samples, we design an OCIL model based on mutual learning with two networks of identical structures. Moreover, a collaborative learning mechanism is developed by aligning the features and class probabilities from the two networks to promote their interaction. Extensive experiments on three widely used datasets for OCIL have demonstrated the effectiveness of our method, obtaining superior performance to state-of-the-art methods. The code is available at https://github.com/susususushi/SDA-MCL.","PeriodicalId":13217,"journal":{"name":"IEEE Transactions on Image Processing","volume":"128 1","pages":""},"PeriodicalIF":10.6,"publicationDate":"2025-10-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145246537","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
S3OIL: Semi-Supervised SAR-to-Optical Image Translation via Multi-Scale and Cross-Set Matching. S3OIL:基于多尺度和交叉集匹配的半监督sar -光学图像转换。
IF 10.6 1区 计算机科学
IEEE Transactions on Image Processing Pub Date : 2025-10-07 DOI: 10.1109/tip.2025.3616576
Xi Yang,Haoyuan Shi,Ziyun Li,Maoying Qiao,Fei Gao,Nannan Wang
{"title":"S3OIL: Semi-Supervised SAR-to-Optical Image Translation via Multi-Scale and Cross-Set Matching.","authors":"Xi Yang,Haoyuan Shi,Ziyun Li,Maoying Qiao,Fei Gao,Nannan Wang","doi":"10.1109/tip.2025.3616576","DOIUrl":"https://doi.org/10.1109/tip.2025.3616576","url":null,"abstract":"Image-to-image translation has achieved great success, but still faces the significant challenge of limited paired data, particularly in translating Synthetic Aperture Radar (SAR) images to optical images. Furthermore, most existing semi-supervised methods place limited emphasis on leveraging the data distribution. To address those challenges, we propose a Semi-Supervised SAR-to-Optical Image Translation (S3OIL) method that achieves high-quality image generation using minimal paired data and extensive unpaired data while strategically exploiting the data distribution. To this end, we first introduce a Cross-Set Alignment Matching (CAM) mechanism to create local correspondences between the generated results of paired and unpaired data, ensuring cross-set consistency. In addition, for unpaired data, we apply weak and strong perturbations and establish intra-set Multi-Scale Matching (MSM) constraints. For paired data, intra-modal semantic consistency (ISC) is presented to ensure alignment with the ground truth. Finally, we propose local and global cross-modal semantic consistency (CSC) to boost structural identity during translation. We conduct extensive experiments on SAR-to-optical datasets and another sketch-to-anime task, demonstrating that S3OIL delivers competitive performance compared to state-of-the-art unsupervised, supervised, and semi-supervised methods, both quantitatively and qualitatively. Ablation studies further reveal that S3OIL can ensure the preservation of both semantic content and structural integrity of the generated images. Our code is available at: https://github.com/XduShi/SOIL.","PeriodicalId":13217,"journal":{"name":"IEEE Transactions on Image Processing","volume":"30 1","pages":""},"PeriodicalIF":10.6,"publicationDate":"2025-10-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145240865","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Robust Single-shot 3D Reconstruction by Sparse-to-dense Stereo Matching and Spline Function based Parallax Modeling. 基于稀疏到密集立体匹配和样条函数视差建模的鲁棒单镜头三维重建。
IF 10.6 1区 计算机科学
IEEE Transactions on Image Processing Pub Date : 2025-10-07 DOI: 10.1109/tip.2025.3616615
ZhenZhou Wang
{"title":"Robust Single-shot 3D Reconstruction by Sparse-to-dense Stereo Matching and Spline Function based Parallax Modeling.","authors":"ZhenZhou Wang","doi":"10.1109/tip.2025.3616615","DOIUrl":"https://doi.org/10.1109/tip.2025.3616615","url":null,"abstract":"Single-shot 3D surface imaging techniques with high accuracy and high resolution are very important in both academia and industry. In this paper, we propose a sparse-to-dense structured light (SL) line-pattern based active stereo vision (ASV) approach to reconstruct the 3D shapes robustly with high-resolution. We propose a sparse-to-dense stereo matching (SDSM) method to solve the challenging problem of line clustering and line matching. We design the structured light line pattern with four colors and the distances between lines of different color range from sparse to dense. Accordingly, the sparse color lines could be clustered and matched at first while the dense color lines could be matched subsequently with the constraint of the clustered and matched sparse color lines. After all the color lines are matched, a spline-function based parallax model (SFPM) is computed based on the points on the matched color lines. Then, the depths of the points in the regions between the color lines are computed by the parallax model. Experimental results show that the proposed SDSM-SFPM ASV approach is more robust than existing methods especially in reconstructing the complex 3D shapes.","PeriodicalId":13217,"journal":{"name":"IEEE Transactions on Image Processing","volume":"16 1","pages":""},"PeriodicalIF":10.6,"publicationDate":"2025-10-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145240877","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
A Shared-memory Parallel Alpha-Tree Algorithm for Extreme Dynamic Ranges. 一种极端动态范围的共享内存并行阿尔法树算法。
IF 10.6 1区 计算机科学
IEEE Transactions on Image Processing Pub Date : 2025-10-07 DOI: 10.1109/tip.2025.3616578
Jiwoo Ryu,Scott C Trager,Michael H F Wilkinson
{"title":"A Shared-memory Parallel Alpha-Tree Algorithm for Extreme Dynamic Ranges.","authors":"Jiwoo Ryu,Scott C Trager,Michael H F Wilkinson","doi":"10.1109/tip.2025.3616578","DOIUrl":"https://doi.org/10.1109/tip.2025.3616578","url":null,"abstract":"The α-tree is an effective hierarchical image representation used for connected filtering or segmentation in remote sensing and other image applications. The α-tree constructs a tree based on the dissimilarities of the pixels in an image. Compared to other hierarchical image representations such as the component tree, the α-tree provides a better representation of the granularity of images and is easier to apply to multichannel images. The major drawback of the α-tree is its processing speed, due to the large amount of data to be processed and the lack of studies on an efficient algorithms, especially on multichannel and high dynamic range images. In this study, we introduce a novel adaptation of the hybrid component tree algorithm on the α-tree for fast parallel α-tree construction in any dynamic range of pixel dissimilarity. We tested the hybrid α-tree algorithm on Sentinel-2 remote sensing images from the European Space Agency (ESA) as well as randomly generated images, on the Hábrók high performance computing cluster. Experimental results show that the hybrid α-tree algorithm achieves the processing speed of 10-30Mpix/s and the speedup of 10-30 on a 128-core computer, proving the efficiency of the first parallel α-tree algorithm in high dynamic range, to the best of our knowledge.","PeriodicalId":13217,"journal":{"name":"IEEE Transactions on Image Processing","volume":"348 1","pages":""},"PeriodicalIF":10.6,"publicationDate":"2025-10-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145240864","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
PFIG-Palm: Controllable Palmprint Generation via Pixel and Feature Identity Guidance. PFIG-Palm:基于像素和特征识别引导的可控掌纹生成。
IF 10.6 1区 计算机科学
IEEE Transactions on Image Processing Pub Date : 2025-10-07 DOI: 10.1109/tip.2025.3616611
Yuchen Zou,Huikai Shao,Chengcheng Liu,Siyu Zhu,Zongqing Hou,Dexing Zhong
{"title":"PFIG-Palm: Controllable Palmprint Generation via Pixel and Feature Identity Guidance.","authors":"Yuchen Zou,Huikai Shao,Chengcheng Liu,Siyu Zhu,Zongqing Hou,Dexing Zhong","doi":"10.1109/tip.2025.3616611","DOIUrl":"https://doi.org/10.1109/tip.2025.3616611","url":null,"abstract":"Palmprint recognition offers a promising solution for convenient and private authentication. However, the scarcity of large-scale palmprint datasets constrains its development and application. Recent approaches have sought to mitigate this issue by synthesizing palmprints based on Bézier curves. Due to the lack of paired data between curves and palmprints, it is difficult to generate curve-driven palmprints with precise identity. To address this challenge, we propose a novel Pixel and Feature Identity Guidance (PFIG) framework to synthesize realistic palmprints, whose IDs are strictly governed by the Bézier curves. In order to establish ID mapping, an ID Injection (IDI) module is constructed to synthesize pseudo-paired data. Two cross-domain ID consistency losses at pixel and feature levels are further proposed to strictly preserve the semantic information of the input ID curves. Experimental results demonstrate that our ID-guided approach can synthesize more realistic palmprints with controllable identities. Based on only 80,000 synthesized palmprints for pre-training, the recognition accuracy can be improved by more than 18% in terms of TAR@1e-6. When trained exclusively on synthetic data, our method achieves superior performance to existing synthetic approaches. The source code is available at https://github.com/YuchenZou/PFIG-Palm.","PeriodicalId":13217,"journal":{"name":"IEEE Transactions on Image Processing","volume":"58 1","pages":""},"PeriodicalIF":10.6,"publicationDate":"2025-10-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145240876","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
AGHL: Anchor-Guided Point Cloud Registration Network with Hybrid Local Feature Perception 基于混合局部特征感知的锚导点云配准网络
IF 10.6 1区 计算机科学
IEEE Transactions on Image Processing Pub Date : 2025-10-01 DOI: 10.1109/tip.2025.3613987
Kun Dai, Tao Xie, Zhiqiang Jiang, Ke Wang, Ruifeng Li, Lijun Zhao, Chuqing Cao
{"title":"AGHL: Anchor-Guided Point Cloud Registration Network with Hybrid Local Feature Perception","authors":"Kun Dai, Tao Xie, Zhiqiang Jiang, Ke Wang, Ruifeng Li, Lijun Zhao, Chuqing Cao","doi":"10.1109/tip.2025.3613987","DOIUrl":"https://doi.org/10.1109/tip.2025.3613987","url":null,"abstract":"","PeriodicalId":13217,"journal":{"name":"IEEE Transactions on Image Processing","volume":"22 1","pages":""},"PeriodicalIF":10.6,"publicationDate":"2025-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145203269","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Optimized Vessel Segmentation: A Structure-Agnostic Approach with Small Vessel Enhancement and Morphological Correction 优化血管分割:小血管增强和形态校正的结构不可知方法
IF 10.6 1区 计算机科学
IEEE Transactions on Image Processing Pub Date : 2025-10-01 DOI: 10.1109/tip.2025.3607583
Dongning Song, Weijian Huang, Jiarun Liu, Md Jahidul Islam, Hao Yang, Shuqiang Wang, Hairong Zheng, Shanshan Wang
{"title":"Optimized Vessel Segmentation: A Structure-Agnostic Approach with Small Vessel Enhancement and Morphological Correction","authors":"Dongning Song, Weijian Huang, Jiarun Liu, Md Jahidul Islam, Hao Yang, Shuqiang Wang, Hairong Zheng, Shanshan Wang","doi":"10.1109/tip.2025.3607583","DOIUrl":"https://doi.org/10.1109/tip.2025.3607583","url":null,"abstract":"","PeriodicalId":13217,"journal":{"name":"IEEE Transactions on Image Processing","volume":"101 1","pages":""},"PeriodicalIF":10.6,"publicationDate":"2025-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145203266","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
BITS: Bit-Extendable Incremental Hashing in Open Environments BITS:开放环境中的位扩展增量哈希
IF 10.6 1区 计算机科学
IEEE Transactions on Image Processing Pub Date : 2025-10-01 DOI: 10.1109/tip.2025.3613924
Yongxin Wang, Zhen-Duo Chen, Xin Luo, Xin-Shun Xu
{"title":"BITS: Bit-Extendable Incremental Hashing in Open Environments","authors":"Yongxin Wang, Zhen-Duo Chen, Xin Luo, Xin-Shun Xu","doi":"10.1109/tip.2025.3613924","DOIUrl":"https://doi.org/10.1109/tip.2025.3613924","url":null,"abstract":"","PeriodicalId":13217,"journal":{"name":"IEEE Transactions on Image Processing","volume":"115 1","pages":""},"PeriodicalIF":10.6,"publicationDate":"2025-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145203267","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Robust Reversible Watermarking with Invisible Distortion Against VAE Watermark Removal 抗VAE水印去除的不可见失真鲁棒可逆水印
IF 10.6 1区 计算机科学
IEEE Transactions on Image Processing Pub Date : 2025-10-01 DOI: 10.1109/tip.2025.3613958
Bobiao Guo, Ping Ping, Fan Liu, Feng Xu
{"title":"Robust Reversible Watermarking with Invisible Distortion Against VAE Watermark Removal","authors":"Bobiao Guo, Ping Ping, Fan Liu, Feng Xu","doi":"10.1109/tip.2025.3613958","DOIUrl":"https://doi.org/10.1109/tip.2025.3613958","url":null,"abstract":"","PeriodicalId":13217,"journal":{"name":"IEEE Transactions on Image Processing","volume":"9 1","pages":""},"PeriodicalIF":10.6,"publicationDate":"2025-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145203268","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Exploring Vision-Based Active 3D Object Detection by Informativeness Characterization 探索基于视觉的主动3D目标检测的信息特征
IF 10.6 1区 计算机科学
IEEE Transactions on Image Processing Pub Date : 2025-10-01 DOI: 10.1109/tip.2025.3613927
Ruixiang Li, Yiming Wu, Yehao Lu, Xuewei Li, Xian Wang, Xiubo Liang, Xi Li
{"title":"Exploring Vision-Based Active 3D Object Detection by Informativeness Characterization","authors":"Ruixiang Li, Yiming Wu, Yehao Lu, Xuewei Li, Xian Wang, Xiubo Liang, Xi Li","doi":"10.1109/tip.2025.3613927","DOIUrl":"https://doi.org/10.1109/tip.2025.3613927","url":null,"abstract":"","PeriodicalId":13217,"journal":{"name":"IEEE Transactions on Image Processing","volume":"104 1","pages":""},"PeriodicalIF":10.6,"publicationDate":"2025-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145203236","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
0
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
相关产品
×
本文献相关产品
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:604180095
Book学术官方微信