IEEE Transactions on Image Processing最新文献

Degradation-Aware Unfolding Network with Sparsity-Gated Experts 稀疏门控专家退化感知展开网络

IF 10.6 1区计算机科学

IEEE Transactions on Image Processing Pub Date : 2026-10-16 DOI: 10.1109/tip.2026.3742676

Zhen Guo, Hongping Gan

引用次数: 0

Combating Pattern and Content Bias: Adversarial Feature Learning for Generalized AI-Generated Image Detection 对抗模式和内容偏见：用于广义人工智能生成图像检测的对抗特征学习

IF 10.6 1区计算机科学

IEEE Transactions on Image Processing Pub Date : 2026-10-16 DOI: 10.1109/tip.2026.3742643

Haifeng Zhang, Qinghui He, Xiuli Bi, Bo Liu, Chi-Man Pun, Bin Xiao

引用次数: 0

Joint Multi-layer Anchor Graph Fusion and Cluster-aware Weight Learning for Multi-view Clustering 面向多视图聚类的联合多层锚图融合与聚类感知权学习

IF 10.6 1区计算机科学

IEEE Transactions on Image Processing Pub Date : 2026-10-14 DOI: 10.1109/tip.2026.3741660

Haonan Xin, Danyang Wu, Jitao Lu, Jiacong Xiao, Huijin Li, Rong Wang, Feiping Nie

引用次数: 0

PRISM-IR: Fine-Grained Expert Selection for All-in-One Image Restoration PRISM-IR：细粒度专家选择的所有在一体的图像恢复

IF 10.6 1区计算机科学

IEEE Transactions on Image Processing Pub Date : 2026-10-09 DOI: 10.1109/tip.2026.3740358

Aiping Zhang, Zongsheng Yue, Yuning Cui, Alois Knoll, Renjing Pei, Wenqi Ren, Xiaochun Cao

引用次数: 0

Satellite-borne Vehicles Tracking: A Motion Position Graph Approach and Dataset 星载车辆跟踪：运动位置图方法与数据集

IF 10.6 1区计算机科学

IEEE Transactions on Image Processing Pub Date : 2026-10-06 DOI: 10.1109/tip.2026.3738352

Jia Shao, Zhi Yin, Jian Zhang, Chen Wu, Bo Du

引用次数: 0

The Scalable Tensor-based Codebook Product Quantization for Multi-Label Image Retrieval 多标签图像检索中基于可伸缩张量的码本积量化

IF 10.6 1区计算机科学

IEEE Transactions on Image Processing Pub Date : 2026-07-09 DOI: 10.1109/tip.2026.3709468

Bin Luo, Laurence T. Yang, Debin Liu, Audrey Yang

引用次数: 0

CycleDiff: Cycle Diffusion Models for Unpaired Image-to-image Translation CycleDiff：非配对图像到图像转换的循环扩散模型

IF 10.6 1区计算机科学

IEEE Transactions on Image Processing Pub Date : 2026-01-30 DOI: 10.1109/tip.2026.3657240

Shilong Zou, Yuhang Huang, Renjiao Yi, Chenyang Zhu, Kai Xu

引用次数: 0

DiWTBR: Dilated Wavelet Transformer for Efficient Megapixel Bokeh Rendering. DiWTBR：用于高效百万像素散景渲染的扩展小波转换器。

IF 10.6 1区计算机科学

IEEE Transactions on Image Processing Pub Date : 2025-11-18 DOI: 10.1109/tip.2025.3632227

Xiaoshi Qiu,Shiyue Yan,Qingmin Liao,Shaojun Liu

{"title":"DiWTBR: Dilated Wavelet Transformer for Efficient Megapixel Bokeh Rendering.","authors":"Xiaoshi Qiu,Shiyue Yan,Qingmin Liao,Shaojun Liu","doi":"10.1109/tip.2025.3632227","DOIUrl":"https://doi.org/10.1109/tip.2025.3632227","url":null,"abstract":"Bokeh is widely used in photography and is traditionally achieved with large-aperture cameras. Bokeh rendering from pictures taken with small-aperture cameras has attracted much attention due to its system simplicity. Most of the existing methods employ Convolutional Neural Networks and often mistakenly blur the foreground due to the limited receptive field. In contrast, Transformers can easily capture long-range dependencies. Therefore, it is more suitable for this problem. However, Transformers suffer from a high computation burden, especially for high-resolution images. In this paper, we propose a Dilated Wavelet Transformer model for Bokeh Rendering (DiWTBR) from a single small-aperture image with megapixels. It employs both window attention and dilated attention schemes, introducing both local and global spatial interactions at a low computation cost. Moreover, to further improve the efficiency, we employ the wavelet transform in the attention block. Experimental results demonstrate that DiWTBR outperforms the state-of-the-art methods by up to 0.7dB in PSNR. Last but not least, our model can be readily implemented on mainstream personal computers and laptops, with only 4G GPU memory consumption. The code will be available on GitHub upon acceptance.","PeriodicalId":13217,"journal":{"name":"IEEE Transactions on Image Processing","volume":"1 1","pages":""},"PeriodicalIF":10.6,"publicationDate":"2025-11-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145545046","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

Dual-Space Topological Isomorphism and Maximization of Predictive Diversity for Unsupervised Domain Adaptation. 双空间拓扑同构与无监督域自适应预测多样性最大化。

IF 10.6 1区计算机科学

IEEE Transactions on Image Processing Pub Date : 2025-09-16 DOI: 10.1109/tip.2025.3608670

Mengru Wang,Jinglei Liu

{"title":"Dual-Space Topological Isomorphism and Maximization of Predictive Diversity for Unsupervised Domain Adaptation.","authors":"Mengru Wang,Jinglei Liu","doi":"10.1109/tip.2025.3608670","DOIUrl":"https://doi.org/10.1109/tip.2025.3608670","url":null,"abstract":"Most existing unsupervised domain adaptation methods rely on explicitly or implicitly aligning the features of source and target domains to construct a domain-invariant space, often using entropy minimization to reduce uncertainty and confusion. However, this approach faces two challenges: 1) Explicit alignment reduces discriminability, while implicit alignment risks pseudo-label noise, making it hard to balance structure preservation and alignment. 2) Sole reliance on entropy minimization can lead to trivial solutions in UDA, where all samples collapse into a single class. To address these issues, we propose Dual-Space Topological Isomorphism and Maximization of Predictive Diversity (DTI-MPD). Topological isomorphism is a continuous, bijective mapping that preserves the topological properties of two spaces, ensuring the global structure and relationships of data remain intact during alignment. Our method aligns source and target domain data in two independent spaces while balancing the effects of entropy minimization through predictive diversity maximization. The core of dual-space topological isomorphism lies in establishing a reversible correspondence between the source and target domains, avoiding information loss during alignment and preserving the global structural and topological characteristics of the data. Meanwhile, predictive diversity maximization mitigates the class collapse caused by entropy minimization, ensuring a more balanced predictive distribution across categories. This approach effectively overcomes the aforementioned issues, enabling better adaptation to new data. Extensive experiments demonstrate that our method achieves state-of-the-art performance on multiple benchmark datasets, validating its effectiveness.","PeriodicalId":13217,"journal":{"name":"IEEE Transactions on Image Processing","volume":"28 1","pages":""},"PeriodicalIF":10.6,"publicationDate":"2025-09-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145071875","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

CAN: Cascade Augmentations against Noise for Image Restoration. CAN：级联增强抗噪声图像恢复。

IF 10.6 1区计算机科学

IEEE Transactions on Image Processing Pub Date : 2025-08-08 DOI: 10.1109/tip.2025.3595374

Yanyang Yan,Siyuan Yao,Wenqi Ren,Rui Zhang,Qi Guo,Xiaochun Cao

{"title":"CAN: Cascade Augmentations against Noise for Image Restoration.","authors":"Yanyang Yan,Siyuan Yao,Wenqi Ren,Rui Zhang,Qi Guo,Xiaochun Cao","doi":"10.1109/tip.2025.3595374","DOIUrl":"https://doi.org/10.1109/tip.2025.3595374","url":null,"abstract":"Image restoration aims to recover the latent clean image from a degraded counterpart. In general, the prevailing state-of-the-art image restoration methods concentrate on solving only a specific degradation type according to the task, e.g. deblurring or deraining. However, if the corresponding well-trained frameworks confront other real-world image corruptions, i.e., the corruptions are not covered in the training phase, and state-of-the-art restoration models will suffer from a lack of generalization ability. We have observed that an image restoration model can be easily confused by noise corruption. Towards improving the robustness of image restoration networks, in this paper, we focus on alleviating the corruption of noise in various image restoration tasks, which is almost inevitable in real-world scenes. To this end, we devise a novel Multifarious Augmentation strategy against Noise (CAN) to enhance the robustness of specific image restoration. Specifically, the given degraded images are sequentially augmented from different perspectives, i.e., noise-aware augmentation, and model-aware augmentation. The noise-aware augmentation is proposed to enrich the samples by introducing various noise operations. Moreover, to adapt to more unknown corruptions, we propose a novel model-aware augmentation mechanism, which enhances the scalability by exploring useful both spatial and frequency clues with the help of model randomness. It is worth noting that the proposed augmentation scheme is model-agnostic, and it can plug and play into arbitrary state-of-the-art image restoration architectures. In addition, we construct noise corruption benchmark datasets, derived from the validation set of standard image restoration datasets, to assist us in evaluating the robustness of restoration networks. Extensive quantitative and qualitative evaluations demonstrate that the proposed method has strong generalization capability which can enhance the robustness of various image restoration frameworks when facing diverse noises.","PeriodicalId":13217,"journal":{"name":"IEEE Transactions on Image Processing","volume":"169 1","pages":""},"PeriodicalIF":10.6,"publicationDate":"2025-08-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144802608","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