Journal of Visual Communication and Image Representation最新文献_第9页

SAST: Semantic-Aware stylized Text-to-Image generation 语义感知的程式化文本到图像的生成

IF 3.1 4区计算机科学

Journal of Visual Communication and Image Representation Pub Date : 2026-01-01 Epub Date: 2025-12-15 DOI: 10.1016/j.jvcir.2025.104685

Xinyue Sun , Jing Guo , yongzhen Ke , Shuai Yang , Kai Wang , Yemeng Wu

{"title":"SAST: Semantic-Aware stylized Text-to-Image generation","authors":"Xinyue Sun , Jing Guo , yongzhen Ke , Shuai Yang , Kai Wang , Yemeng Wu","doi":"10.1016/j.jvcir.2025.104685","DOIUrl":"10.1016/j.jvcir.2025.104685","url":null,"abstract":"<div><div>The pre-trained text-to-image diffusion probabilistic model has achieved excellent quality, providing users with good visual effects and attracting many users to use creative text to control the generated results. For users’ detailed generation requirements, using reference images to “stylize” text-to-image is more common because they cannot be fully explained in limited language. However, there is a style deviation between the images generated by existing methods and the style reference images, contrary to the human perception that similar semantic object regions in two images with the same style should share style. To solve this problem, this paper proposes a semantic-aware style transfer method (SAST) to strengthen the semantic-level style alignment between the generated image and style reference image. First, we lead language-driven semantic segmentation trained on the COCO dataset into a general style transfer model to capture the mask that the text in the style reference image focuses on. Similarly, we use the same text to perform mask extraction on the cross-attention layer of the text-to-image model. Based on the two obtained mask maps, we modify the self-attention layer in the diffusion model to control the injection process of style features. Experiments show that we achieve better style fidelity and style alignment metrics, indicating that the generated images are more consistent with human perception. Code is available at https://gitee.com/yongzhenke/SAST. Additional Keywords and <strong>Phrases</strong>:Text-to-image, Image style transfer, Diffusion model, Semantic alignment.</div></div>","PeriodicalId":54755,"journal":{"name":"Journal of Visual Communication and Image Representation","volume":"115 ","pages":"Article 104685"},"PeriodicalIF":3.1,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145797418","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"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

CQR-UC: A color QR code-based underwater wireless communication method with GAN-based image enhancement CQR-UC：一种基于gan图像增强的彩色二维码水下无线通信方法

IF 3.1 4区计算机科学

Journal of Visual Communication and Image Representation Pub Date : 2026-01-01 Epub Date: 2026-01-08 DOI: 10.1016/j.jvcir.2026.104708

Zheng Zhao , Yufan Feng , Shangxin Li , Qian Mao , Tingwei Chen , Qi Zhao , Xiaoya Fan

引用次数: 0

CrossGlue: Cross-Modal Image matching via potential message investigation and visual-gradient message integration 交叉胶：跨模态图像匹配通过潜在的信息调查和视觉梯度信息集成

IF 3.1 4区计算机科学

Journal of Visual Communication and Image Representation Pub Date : 2026-01-01 Epub Date: 2025-11-03 DOI: 10.1016/j.jvcir.2025.104620

Chaobo Yu , Zhonghui Pei , Xiaoran Wang , Huabing Zhou

{"title":"CrossGlue: Cross-Modal Image matching via potential message investigation and visual-gradient message integration","authors":"Chaobo Yu , Zhonghui Pei , Xiaoran Wang , Huabing Zhou","doi":"10.1016/j.jvcir.2025.104620","DOIUrl":"10.1016/j.jvcir.2025.104620","url":null,"abstract":"<div><div>Compared with single-modal image matching, cross-modal image matching can provide more comprehensive and detailed information, which is essential for a series of visual-related tasks. However, the matching process is difficult due to differences in imaging principles, proportions, relative translation and rotation between visible and infrared images. Besides, other detection-based single-modal matching methods have low accuracy, while detection-free methods are time-consuming and fail to handle real-world scenarios. Therefore, this paper proposes CrossGlue, a light cross-modal images matching framework. The framework introduces a cross-modal message transfer (CMT) module to integrate more potential information for each keypoint through one-to-one image transfer, and a visual-gradient graph neural network (VG-GNN) to enhance visible–infrared matching in degraded scenarios. Experimental results on public datasets show that CrossGlue has excellent performance among detection-based methods and outperforms strong baseline methods in tasks such as homography estimation and relative pose estimation.</div></div>","PeriodicalId":54755,"journal":{"name":"Journal of Visual Communication and Image Representation","volume":"114 ","pages":"Article 104620"},"PeriodicalIF":3.1,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145468802","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"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 two-stage tone mapping network based on attention mechanism for high dynamic range images 基于注意机制的高动态范围图像两阶段色调映射网络

IF 3.1 4区计算机科学

Journal of Visual Communication and Image Representation Pub Date : 2026-01-01 Epub Date: 2025-12-03 DOI: 10.1016/j.jvcir.2025.104672

Mingtao Zhu , Hengyong Yu , Ying Chu

{"title":"A two-stage tone mapping network based on attention mechanism for high dynamic range images","authors":"Mingtao Zhu , Hengyong Yu , Ying Chu","doi":"10.1016/j.jvcir.2025.104672","DOIUrl":"10.1016/j.jvcir.2025.104672","url":null,"abstract":"<div><div>High dynamic range (HDR) imaging enhances visual realism by capturing a wide luminance range, but displaying HDR images on devices with limited dynamic range requires effective tone mapping operators (TMOs). We propose a novel two-stage tone mapping network (TSTMNet) based on attention mechanisms to address this challenge. The first stage utilizes adaptive luminance modulation blocks based on channel attention to dynamically adjust global luminance, achieving adaptive luminance modification. The second stage integrates local enhancement transformer blocks, leveraging self-attention from Transformers to enhance local details. This combination allows the TSTMNet to utilize the strengths of both CNNs and Transformers, overcoming their individual limitations in modeling long-range dependencies and preserving local details. Extensive experiments demonstrate that TSTMNet achieves competitive performance among recent methods in both quantitative metrics and qualitative visual quality, achieving superior dynamic range compression and detail preservation. Our method offers a robust and efficient solution to the tone mapping problem. The code is available to be downloaded at <span><span>https://github.com/mtlaa/TSTMNet</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":54755,"journal":{"name":"Journal of Visual Communication and Image Representation","volume":"115 ","pages":"Article 104672"},"PeriodicalIF":3.1,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145694271","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"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

Future object localization using multi-modal ego-centric video 未来使用多模态以自我为中心的视频进行对象定位

IF 3.1 4区计算机科学

Journal of Visual Communication and Image Representation Pub Date : 2026-01-01 Epub Date: 2025-12-18 DOI: 10.1016/j.jvcir.2025.104684

Jee-Ye Yoon , Je-Won Kang

{"title":"Future object localization using multi-modal ego-centric video","authors":"Jee-Ye Yoon , Je-Won Kang","doi":"10.1016/j.jvcir.2025.104684","DOIUrl":"10.1016/j.jvcir.2025.104684","url":null,"abstract":"<div><div>Future object localization (FOL) seeks to predict the future locations of objects using information from past and present video frames. Ego-centric videos from vehicle-mounted cameras serve as a key source. However, these videos are constrained by a limited field of view and susceptibility to external conditions. To address these challenges, this paper presents a novel FOL approach that combines ego-centric video data with point cloud data, enhancing both robustness and accuracy. The proposed model is based on a deep neural network that prioritizes front-camera ego-centric videos, exploiting their rich visual cues. By integrating point cloud data, the system improves three-dimensional (3D) object localization. Furthermore, the paper introduces a novel method for ego-motion prediction. The ego-motion prediction network employs multi-modal sensors to comprehensively capture physical displacement in both 2D and 3D spaces, effectively handling occlusions and the limited perspective inherent in ego-centric videos. Experimental results indicate that the proposed FOL system with ego-motion prediction (MS-FOLe) outperforms existing methods on large-scale open datasets for intelligent driving.</div></div>","PeriodicalId":54755,"journal":{"name":"Journal of Visual Communication and Image Representation","volume":"115 ","pages":"Article 104684"},"PeriodicalIF":3.1,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145797412","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"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

3D human pose estimation based on a Hybrid approach of Transformer and GCN-Former 基于Transformer和GCN-Former混合方法的三维人体姿态估计

IF 3.1 4区计算机科学

Journal of Visual Communication and Image Representation Pub Date : 2026-01-01 Epub Date: 2025-12-31 DOI: 10.1016/j.jvcir.2025.104696

Xiaojian Pan , Guang Li , Ningfei Zhang , Jianjun Li

{"title":"3D human pose estimation based on a Hybrid approach of Transformer and GCN-Former","authors":"Xiaojian Pan , Guang Li , Ningfei Zhang , Jianjun Li","doi":"10.1016/j.jvcir.2025.104696","DOIUrl":"10.1016/j.jvcir.2025.104696","url":null,"abstract":"<div><div>Recently, self-supervised pretraining paradigms have been extensively investigated in the domain of skeleton-based 3D human pose estimation. Especially, methods based on masked prediction have elevated the performance of pretraining to new heights. The proposed two-stage model aims to capture richer and more significant information. Specifically, the pretraining module is designed to extract enhanced representations, and the Hybrid Dual-Stream Spatio-Temporal Network (HDSTN) processes these representations to recover detailed 3D pose information. In the pretraining phase, an improved teacher model uses the original input data to generate prediction targets for the student model. The proposed Hybrid Dual-Stream Spatio-Temporal Network (HDSTN) integrates Transformer-GCNFormer (TGFormer) blocks, which employ two parallel processing streams. The Transformer stream captures long-range dependencies, while the GCNFormer stream focuses on learning local spatial–temporal relationships between joints. By combining the strengths of both approaches, TGFormer reduces dimensionality efficiently and provides a more comprehensive representation of the 3D human pose structure. The local relationships between adjacent joints are leveraged by the GCNFormer module to generate a new representation that complements the Transformer’s output. By adaptively fusing these two representations, TGFormer demonstrates enhanced capability in learning the underlying 3D structure. This manuscript extends our earlier conference paper [Zhang et al., 2024 (AIHCIR)] which introduced a two-stage transformer-based pipeline for 3D pose lifting.</div></div>","PeriodicalId":54755,"journal":{"name":"Journal of Visual Communication and Image Representation","volume":"115 ","pages":"Article 104696"},"PeriodicalIF":3.1,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145884213","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"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

USRNet: A simple yet effective Underwater Scene Restoration Network USRNet：一个简单而有效的水下场景恢复网络

IF 3.1 4区计算机科学

Journal of Visual Communication and Image Representation Pub Date : 2026-01-01 Epub Date: 2026-01-08 DOI: 10.1016/j.jvcir.2026.104710

Shabnam Thakur, Jhilik Bhattacharya, Shailendra Tiwari

{"title":"USRNet: A simple yet effective Underwater Scene Restoration Network","authors":"Shabnam Thakur, Jhilik Bhattacharya, Shailendra Tiwari","doi":"10.1016/j.jvcir.2026.104710","DOIUrl":"10.1016/j.jvcir.2026.104710","url":null,"abstract":"<div><div>Ensuring clear underwater visibility is crucial for disciplines such as marine robotics, oceanography and marine biology, which require fast, high-quality image processing to support real-time analysis. This paper introduces the Underwater Scene Restoration network(USRNet), whose efficient and lightweight architecture overcomes critical limitations of prior work. Unlike current approaches that separately estimate complex, underwater optical parameters, our proposed USRNet employs an end-to-end approach to jointly estimate the Transmission Map(TM) and Background Light(BL) within the network using a reformulated version of the Atmospheric Scattering Model(ASM). The USRNet consists of two distinct modules. First, a dedicated Color Cast Removal(CCR) module to neutralize color casts by learning the the inherent color shifts in underwater scenes. Second, the Scene Radiance Estimation (SRE) module, which focuses on reconstructing a high-quality approximation of the final restored image. Comprehensive evaluations across multiple datasets validate our approach in both quantitative and qualitative metrics.</div></div>","PeriodicalId":54755,"journal":{"name":"Journal of Visual Communication and Image Representation","volume":"115 ","pages":"Article 104710"},"PeriodicalIF":3.1,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145976544","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"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

Enhancing 3D point cloud generation via Mamba-based time-varying denoising diffusion 通过基于曼巴的时变去噪扩散增强三维点云生成

IF 3.1 4区计算机科学

Journal of Visual Communication and Image Representation Pub Date : 2026-01-01 Epub Date: 2025-11-27 DOI: 10.1016/j.jvcir.2025.104657

DaoPeng Zhang, Li Yu

{"title":"Enhancing 3D point cloud generation via Mamba-based time-varying denoising diffusion","authors":"DaoPeng Zhang, Li Yu","doi":"10.1016/j.jvcir.2025.104657","DOIUrl":"10.1016/j.jvcir.2025.104657","url":null,"abstract":"<div><div>3D point cloud generation plays a pivotal role in a wide range of applications, including robotics, medical imaging, autonomous driving, and virtual/augmented reality (VR/AR). However, generating high-quality point clouds remains highly challenging due to the irregularity and unordered nature of point cloud data. Existing Transformer-based generative models suffer from quadratic computational complexity, which limits their ability to capture global contextual dependencies and often leads to the loss of critical geometric information. To address these limitations, we propose a novel diffusion-based framework for point cloud generation that integrates the Mamba state-space model — known for its linear complexity and strong long-sequence modeling capability — with convolutional layers. Specifically, Mamba is employed to capture global structural dependencies across time steps, while the convolutional layers refine local geometric details. To effectively leverage the strengths of both components, we introduce a learnable masking mechanism that dynamically fuses global and local features at optimal time steps, thereby exploiting their complementary advantages. Extensive experiments demonstrate that our model outperforms previous point cloud generative approaches such as TIGER and PVD in terms of both quality and diversity. On the airplane category, our model achieves a 9.28% improvement in 1-NNA accuracy based on EMD compared to PVD, and a 1.72% improvement based on CD compared to TIGER. Compared with recent baseline models, our method consistently achieves significant gains across multiple evaluation metrics.</div></div>","PeriodicalId":54755,"journal":{"name":"Journal of Visual Communication and Image Representation","volume":"115 ","pages":"Article 104657"},"PeriodicalIF":3.1,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145610298","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"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

PatchNeRF: Patch-based Neural Radiance Fields for real time view synthesis in wide-scale scenes PatchNeRF：基于patch的神经辐射场，用于大尺度场景的实时视图合成

IF 3.1 4区计算机科学

Journal of Visual Communication and Image Representation Pub Date : 2026-01-01 Epub Date: 2025-11-20 DOI: 10.1016/j.jvcir.2025.104602

Ziyu Hu , Xiaoguang Jiang , Qiong Liu , Xin Ding

引用次数: 0

CAE-Net: Generalized deepfake image detection using convolution and attention mechanisms with spatial and frequency domain features CAE-Net：基于空间和频域特征的卷积和注意机制的广义深度假图像检测

IF 3.1 4区计算机科学

Journal of Visual Communication and Image Representation Pub Date : 2026-01-01 Epub Date: 2025-12-19 DOI: 10.1016/j.jvcir.2025.104679

Anindya Bhattacharjee , Kaidul Islam , Kafi Anan , Ashir Intesher , Abrar Assaeem Fuad , Utsab Saha , Hafiz Imtiaz

{"title":"CAE-Net: Generalized deepfake image detection using convolution and attention mechanisms with spatial and frequency domain features","authors":"Anindya Bhattacharjee , Kaidul Islam , Kafi Anan , Ashir Intesher , Abrar Assaeem Fuad , Utsab Saha , Hafiz Imtiaz","doi":"10.1016/j.jvcir.2025.104679","DOIUrl":"10.1016/j.jvcir.2025.104679","url":null,"abstract":"<div><div>The spread of deepfakes poses significant security concerns, demanding reliable detection methods. However, diverse generation techniques and class imbalance in datasets create challenges. We propose CAE-Net, a Convolution- and Attention-based weighted Ensemble network combining spatial and frequency-domain features for effective deepfake detection. The architecture integrates EfficientNet, Data-Efficient Image Transformer (DeiT), and ConvNeXt with wavelet features to learn complementary representations. We evaluated CAE-Net on the diverse IEEE Signal Processing Cup 2025 (DF-Wild Cup) dataset, which has a 5:1 fake-to-real class imbalance. To address this, we introduce a multistage disjoint-subset training strategy, sequentially training the model on non-overlapping subsets of the fake class while retaining knowledge across stages. Our approach achieved 94.46% accuracy and a 97.60% AUC, outperforming conventional class-balancing methods. Visualizations confirm the network focuses on meaningful facial regions, and our ensemble design demonstrates robustness against adversarial attacks, positioning CAE-Net as a dependable and generalized deepfake detection framework.</div></div>","PeriodicalId":54755,"journal":{"name":"Journal of Visual Communication and Image Representation","volume":"115 ","pages":"Article 104679"},"PeriodicalIF":3.1,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145840339","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"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