Journal of Visual Communication and Image Representation最新文献_第10页

Copy-move forgery detection of social media images using tendency sparsity filtering and variable cluster spectral clustering 基于趋势稀疏滤波和可变聚类光谱聚类的社交媒体图像复制-移动伪造检测

IF 3.1 4区计算机科学

Journal of Visual Communication and Image Representation Pub Date : 2026-01-01 Epub Date: 2025-11-07 DOI: 10.1016/j.jvcir.2025.104635

Cong Lin , Hai Yang , Ke Huang , Daqiang Long , Yuke Zhong , Yuqiao Deng , Yamin Wen

{"title":"Copy-move forgery detection of social media images using tendency sparsity filtering and variable cluster spectral clustering","authors":"Cong Lin , Hai Yang , Ke Huang , Daqiang Long , Yuke Zhong , Yuqiao Deng , Yamin Wen","doi":"10.1016/j.jvcir.2025.104635","DOIUrl":"10.1016/j.jvcir.2025.104635","url":null,"abstract":"<div><div>Copy-move forgery is a common way of image tampering. In reality, most of the images encountered are compressed by social media. Based on this, a copy-move forgery detection method of social media images based on tendency sparsity (TS) filtering and variable cluster spectral clustering (VCS clustering) is proposed. First, we normalize the image scale to obtain the sufficient number of keypoints. To accelerate the matching speed, the hierarchical matching method is adopted. Next, the TS filtering is applied to remove the preference set (PS) vectors that do not meet the condition. To estimate the good affine transformation, the PS vectors are clustered using the VCS clustering. Finally, the tampering location result is output. Through comparative experiments on several public uncompressed datasets, as well as datasets compressed by social media, it has been proven that the proposed method has good robustness in detecting social media images, outperforming the state-of-the-art methods.</div></div>","PeriodicalId":54755,"journal":{"name":"Journal of Visual Communication and Image Representation","volume":"114 ","pages":"Article 104635"},"PeriodicalIF":3.1,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145520917","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

Fast adaptive QTMT partitioning for intra 360°video coding based on gradient boosted trees 基于梯度增强树的360°视频编码快速自适应QTMT分割

IF 3.1 4区计算机科学

Journal of Visual Communication and Image Representation Pub Date : 2026-01-01 Epub Date: 2025-11-06 DOI: 10.1016/j.jvcir.2025.104629

Jose N. Filipe , Luis M.N. Tavora , Sergio M.M. Faria , Antonio Navarro , Pedro A.A. Assuncao

{"title":"Fast adaptive QTMT partitioning for intra 360°video coding based on gradient boosted trees","authors":"Jose N. Filipe , Luis M.N. Tavora , Sergio M.M. Faria , Antonio Navarro , Pedro A.A. Assuncao","doi":"10.1016/j.jvcir.2025.104629","DOIUrl":"10.1016/j.jvcir.2025.104629","url":null,"abstract":"<div><div>The rising demand for UHD and 360°content has driven the creation of advanced compression tools with enhanced coding efficiency. Versatile Video Coding (VVC) has recently improved coding efficiency over previous standards, but introduces significantly higher computational complexity. To address this, this paper presents a novel intra-coding method for 360°video in Equirectangular Projection (ERP) format that reduces complexity with minimal impact on coding efficiency. It shows that the North, Equator, and South regions of ERP images exhibit distinct complexity and spatial characteristics. A region-based approach uses multiple Gradient Boosted Trees models for each region to determine if a partition type can be skipped. Additionally, an adaptive decision threshold scheme is introduced to optimise vertical partitioning in polar regions. The paper also presents an optimisation solution for the Complexity/BD-Rate loss trade-off parameters. Experimental results demonstrate a 50% complexity gain with only a 0.37% BD-Rate loss, outperforming current state-of-the-art methods.</div></div>","PeriodicalId":54755,"journal":{"name":"Journal of Visual Communication and Image Representation","volume":"114 ","pages":"Article 104629"},"PeriodicalIF":3.1,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145520898","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

DAIRNet: Degradation-aware All-in-one Image Restoration Network with cross-channel feature interaction DAIRNet：具有跨通道特征交互的退化感知一体化图像恢复网络

IF 3.1 4区计算机科学

Journal of Visual Communication and Image Representation Pub Date : 2026-01-01 Epub Date: 2025-11-29 DOI: 10.1016/j.jvcir.2025.104659

Amit Monga , Hemkant Nehete , Tharun Kumar Reddy Bollu , Balasubramanian Raman

{"title":"DAIRNet: Degradation-aware All-in-one Image Restoration Network with cross-channel feature interaction","authors":"Amit Monga , Hemkant Nehete , Tharun Kumar Reddy Bollu , Balasubramanian Raman","doi":"10.1016/j.jvcir.2025.104659","DOIUrl":"10.1016/j.jvcir.2025.104659","url":null,"abstract":"<div><div>Image restoration is a fundamental task in computer vision that recovers clean images from degraded inputs. However, preserving fine-details and maintaining global structural consistency are challenging tasks. Traditional convolutional neural network (CNN)-based methods capture local features but fail to model long-range dependencies and often overlook small objects within similar backgrounds. Transformers, conversely, model global context effectively but lack local detail precision. To overcome these limitations, this paper proposes a Degradation-aware All-in-one Image Restoration Network that integrates both CNNs and Transformers. Beginning with a multiscale feature extraction block, the network captures diverse features across different resolutions, enhancing its ability to handle complex image structures. The features from CNN encoder are subsequently passed through a Transformer decoder. Notably, an interleaved Transformer is applied to the features extracted by the CNN encoder, fostering cross-interaction between features and helping to propagate similar texture signals across the entire feature space, making them more distinguishable. These improved features are then concatenated with the transformer decoder blocks with degradation-aware information as prompts, enriching the restoration process. On average, across various restoration tasks, DAIRNet surpasses the state-of-the-art PromptIR and AirNet methods by 0.76 dB and 1.62 dB, respectively. Specifically, it achieves gains of 1.74 dB in image deraining, 0.26 dB in high-noise level denoising, and 0.84 dB in image dehazing tasks as compared to PromptIR. Single-task benchmarks further confirm the model’s effectiveness and generalizability.</div></div>","PeriodicalId":54755,"journal":{"name":"Journal of Visual Communication and Image Representation","volume":"115 ","pages":"Article 104659"},"PeriodicalIF":3.1,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145694283","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

Multi-scale interleaved transformer network for image deraining 多尺度交错变压器网络图像提取

IF 3.1 4区计算机科学

Journal of Visual Communication and Image Representation Pub Date : 2026-01-01 Epub Date: 2025-11-25 DOI: 10.1016/j.jvcir.2025.104655

Yue Que , Chen Qiu , Hanqing Xiong , Xue Xia , Zhiwei Liu

{"title":"Multi-scale interleaved transformer network for image deraining","authors":"Yue Que , Chen Qiu , Hanqing Xiong , Xue Xia , Zhiwei Liu","doi":"10.1016/j.jvcir.2025.104655","DOIUrl":"10.1016/j.jvcir.2025.104655","url":null,"abstract":"<div><div>Convolutional neural networks (CNNs) have demonstrated impressive performance in image deraining tasks. However, CNNs have a limited receptive field, which restricts their ability to adapt to spatial variations in the input. Recently, Transformers have demonstrated promising results in image deraining, surpassing CNN in several cases. However, most existing methods leverage limited spatial input information through attribution analysis. In this paper, we investigated the construction of multi-scale feature representations within Transformers to fully exploit their potential in image deraining. We propose a multi-scale interleaved Transformer framework, which aims to reconstruct high-quality images by leveraging information across different scales, thereby enabling it to better capture the size and distribution of rain. In addition, we introduce a hybrid cross-attention mechanism to replace traditional feature fusion, facilitating global feature interaction and capturing complementary information across scales simultaneously. Our approach surpasses state-of-the-art methods in terms of image deraining performance on two types of benchmark datasets.</div></div>","PeriodicalId":54755,"journal":{"name":"Journal of Visual Communication and Image Representation","volume":"115 ","pages":"Article 104655"},"PeriodicalIF":3.1,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145694272","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

Gradient degradation-aware rate control for VVC using Nash equilibrium 基于纳什均衡的梯度退化感知VVC速率控制

IF 3.1 4区计算机科学

Journal of Visual Communication and Image Representation Pub Date : 2026-01-01 Epub Date: 2026-01-03 DOI: 10.1016/j.jvcir.2025.104705

Chenpeng Lu , Huanqiang Zeng , Chao Jiao , Jing Chen , Qi Lin , Huijie Zheng

{"title":"Gradient degradation-aware rate control for VVC using Nash equilibrium","authors":"Chenpeng Lu , Huanqiang Zeng , Chao Jiao , Jing Chen , Qi Lin , Huijie Zheng","doi":"10.1016/j.jvcir.2025.104705","DOIUrl":"10.1016/j.jvcir.2025.104705","url":null,"abstract":"<div><div>The <span><math><mi>λ</mi></math></span>-domain rate control in Versatile Video Coding (VVC) achieves remarkable performance, enabling higher visual quality under stringent bit constraints. However, for high-resolution video, a significant portion of Coding Tree Units (CTUs) are the skipped block, and using the updated parameters from skip CTUs may lead to unreasonable bit allocation. This paper presents a novel rate control method for VVC that separately allocates bits to skip and non-skip CTUs. Skip CTUs’ bits can be calculated at once based on the frame’s target bits per pixel (bpp) without separate calculations. For non-skip CTUs, we first formulate the bit allocation problem as a Nash equilibrium problem inspired by the definition of the game theory. Subsequently, we design a utility function based on Gradient Magnitude Similarity Deviation (GMSD) to quantify the degradation in gradient information caused by encoding. The <span><math><mi>λ</mi></math></span> parameter for bit allocation of non-skip CTUs is calculated accordingly. The proposed method is implemented in VTM 13.0, and experimental results confirm its effectiveness in enhancing visual quality and achieving significant reductions in bit rates.</div></div>","PeriodicalId":54755,"journal":{"name":"Journal of Visual Communication and Image Representation","volume":"115 ","pages":"Article 104705"},"PeriodicalIF":3.1,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145925005","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

CSCA: Channel-specific information contrast and aggregation for weakly supervised semantic segmentation 弱监督语义分割的特定于信道的信息对比和聚合

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.2025.104690

Guoqing Zhang , Wenxin Sun , Long Wang , Yuhui Zheng , Zhonglin Ye

{"title":"CSCA: Channel-specific information contrast and aggregation for weakly supervised semantic segmentation","authors":"Guoqing Zhang , Wenxin Sun , Long Wang , Yuhui Zheng , Zhonglin Ye","doi":"10.1016/j.jvcir.2025.104690","DOIUrl":"10.1016/j.jvcir.2025.104690","url":null,"abstract":"<div><div>Existing multi-stage weakly supervised semantic segmentation (WSSS) methods typically use refined class activation maps (CAMs) to generate pseudo labels. However, CAMs are prone to misactivating background regions associated with foreground objects (e.g., train and railroad). Some previous efforts introduce additional supervisory signals as background cues but do not consider the rich foreground–background discrimination insights present in different channels of CAMs. In this work, we present a novel framework that explicitly models channel-specific information to enhance foreground–background discrimination and contextual understanding in CAM generation. By effectively capturing and integrating channel-wise local and global cues, our approach mitigates common misactivation issues without requiring additional supervision. Experiments on the PASCAL VOC 2012 dataset show that our method alleviates misactivation in CAMs without additional supervision, providing significant improvements over off-the-shelf methods and achieving strong segmentation performance.</div></div>","PeriodicalId":54755,"journal":{"name":"Journal of Visual Communication and Image Representation","volume":"115 ","pages":"Article 104690"},"PeriodicalIF":3.1,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145840345","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

Mitigating bias in Few Shot Class Incremental Learning with Feature Augmentation and Logits Mix-up 基于特征增强和Logits混合的少镜头类增量学习中的偏差缓解

IF 3.1 4区计算机科学

Journal of Visual Communication and Image Representation Pub Date : 2026-01-01 Epub Date: 2025-12-24 DOI: 10.1016/j.jvcir.2025.104687

Krishna Kumar Singh, K. Hima Bindu

{"title":"Mitigating bias in Few Shot Class Incremental Learning with Feature Augmentation and Logits Mix-up","authors":"Krishna Kumar Singh, K. Hima Bindu","doi":"10.1016/j.jvcir.2025.104687","DOIUrl":"10.1016/j.jvcir.2025.104687","url":null,"abstract":"<div><div>Deep neural networks often excel at specific tasks, but face significant challenges with catastrophic forgetting when learning new classes incrementally. Recent state-of-the-art approaches in Few-Shot Class-Incremental Learning (FSCIL) predominantly utilize a decoupled classifier functioning as a prototype-only head after pre-training. The prototypes for new classes, derived from limited instances, tend to exhibit higher bias, resulting in poor performance at incremental classes. Recent methods have approached this issue through various prototype calibrations or classifier fine-tuning. However, they have limited improvements during incremental sessions.</div><div>This paper presents a novel approach to mitigate bias through <strong>F</strong>eature <strong>A</strong>ugmentation and an entropy-weighted <strong>L</strong>ogits <strong>M</strong>ix-up (FALM) method. The embedding derived from the final layer of the feature extractor is task-specific and may overlook certain features of unseen classes. This work incorporates a missing-pass filter applied to the features of an intermediate layer to augment the embedding of the final layer. Additionally, a logits mix-up is employed to reduce bias further by applying an entropy-weighted combination of logits resulting from three separate heads. Experiments on the miniImageNet, CIFAR100, and CUB200 datasets show that FALM achieves better performance compared to state-of-the-art (SOTA) models.</div></div>","PeriodicalId":54755,"journal":{"name":"Journal of Visual Communication and Image Representation","volume":"115 ","pages":"Article 104687"},"PeriodicalIF":3.1,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145840340","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

Structure-aware filter using self-guided information 使用自引导信息的结构感知过滤器

IF 3.1 4区计算机科学

Journal of Visual Communication and Image Representation Pub Date : 2026-01-01 Epub Date: 2025-12-05 DOI: 10.1016/j.jvcir.2025.104677

Mukhalad Al-nasrawi , Guang Deng , Riyadh Nazar Ali Algburi

引用次数: 0

SSUFormer: Spatial–spectral UnetFormer for improving hyperspectral image classification SSUFormer：用于改进高光谱图像分类的空间光谱UnetFormer

IF 3.1 4区计算机科学

Journal of Visual Communication and Image Representation Pub Date : 2026-01-01 Epub Date: 2025-11-04 DOI: 10.1016/j.jvcir.2025.104633

Thuan Minh Nguyen , Khoi Anh Bui , Myungsik Yoo

引用次数: 0

Machine learning and transformers for thyroid carcinoma diagnosis 甲状腺癌诊断的机器学习和变压器

IF 3.1 4区计算机科学

Journal of Visual Communication and Image Representation Pub Date : 2026-01-01 Epub Date: 2025-12-11 DOI: 10.1016/j.jvcir.2025.104668

Yassine Habchi , Hamza Kheddar , Yassine Himeur , Mohamed Chahine Ghanem

{"title":"Machine learning and transformers for thyroid carcinoma diagnosis","authors":"Yassine Habchi , Hamza Kheddar , Yassine Himeur , Mohamed Chahine Ghanem","doi":"10.1016/j.jvcir.2025.104668","DOIUrl":"10.1016/j.jvcir.2025.104668","url":null,"abstract":"<div><div>Thyroid carcinoma (TC) remains a critical health challenge, where timely and accurate diagnosis is essential for improving patient outcomes. This review provides a comprehensive examination of artificial intelligence (AI) applications — including machine learning (ML), deep learning (DL), and emerging transformer-based approaches — in the detection and classification of TC. We first outline standardized evaluation metrics and analyze publicly available datasets, highlighting their limitations in diversity, annotation quality, and representativeness. Next, we survey AI-driven diagnostic frameworks across three domains: classification, segmentation, and prediction, with emphasis on ultrasound imaging, histopathology, and genomics. A comparative analysis of ML and DL approaches illustrates their respective strengths, such as interpretability in smaller datasets versus automated feature extraction in large-scale imaging tasks. Advanced methods leveraging vision transformers (ViT) and large language models (LLMs) are discussed alongside traditional models, situating them within a broader ecosystem of feature engineering, ensemble learning, and hybrid strategies. We also examine key challenges — imbalanced datasets, computational demands, model generalizability, and ethical concerns — before outlining future research directions, including explainable AI, federated and privacy-preserving learning, reinforcement learning, and integration with the Internet of Medical Things (IoMT). By bridging technical insights with clinical considerations, this review establishes a roadmap for next-generation TC diagnostics and highlights pathways toward robust, patient-centric, and ethically responsible AI deployment in oncology.</div></div>","PeriodicalId":54755,"journal":{"name":"Journal of Visual Communication and Image Representation","volume":"115 ","pages":"Article 104668"},"PeriodicalIF":3.1,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145797410","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