IEEE Transactions on Image Processing最新文献_第3页

Content-decoupled Contrastive Learning-based Implicit Degradation Modeling for Blind Image Super-Resolution Supplementary Material 基于内容解耦对比学习的盲图像超分辨率补充材料隐式退化建模

IF 10.6 1区计算机科学

IEEE Transactions on Image Processing Pub Date : 2025-04-11 DOI: 10.1109/tip.2025.3558442

Jiang Yuan, Ji Ma, Bo Wang, Weiming Hu

引用次数: 0

Combining Pre- and Post-Demosaicking Noise Removal for RAW Video 结合Pre- and - post - demosaked Noise Removal for RAW视频

IF 10.6 1区计算机科学

IEEE Transactions on Image Processing Pub Date : 2025-01-15 DOI: 10.1109/tip.2025.3527886

M. Sánchez-Beeckman, A. Buades, N. Brandonisio, B. Kanoun

引用次数: 0

Harnessing Multi-modal Large Language Models for Measuring and Interpreting Color Differences 利用多模态大语言模型测量和解释色差

IF 10.6 1区计算机科学

IEEE Transactions on Image Processing Pub Date : 2025-01-01 DOI: 10.1109/tip.2024.3522802

Zhihua Wang, Yu Long, Qiuping Jiang, Chao Huang, Xiaochun Cao

引用次数: 0

SDCoT++: Improved Static-Dynamic Co-Teaching for Class-Incremental 3D Object Detection SDCoT++：改进的静态-动态协同教学——增量三维物体检测

IF 10.6 1区计算机科学

IEEE Transactions on Image Processing Pub Date : 2024-12-31 DOI: 10.1109/tip.2024.3518774

Na Zhao, Peisheng Qian, Fang Wu, Xun Xu, Xulei Yang, Gim Hee Lee

引用次数: 0

Nonconvex Robust High-Order Tensor Completion Using Randomized Low-Rank Approximation 使用随机低库近似的非凸稳健高阶张量补全

IF 10.6 1区计算机科学

IEEE Transactions on Image Processing Pub Date : 2024-04-10 DOI: 10.1109/tip.2024.3385284

Wenjin Qin, Hailin Wang, Feng Zhang, Weijun Ma, Jianjun Wang, Tingwen Huang

引用次数: 0

Field-of-View IoU for Object Detection in 360° Images. 用于 360° 图像中物体检测的视场 IoU。

IF 10.6 1区计算机科学

IEEE Transactions on Image Processing Pub Date : 2023-07-21 DOI: 10.1109/TIP.2023.3296013

Miao Cao, Satoshi Ikehata, Kiyoharu Aizawa

引用次数: 0

TGFuse: An Infrared and Visible Image Fusion Approach Based on Transformer and Generative Adversarial Network. TGFuse：基于变换器和生成对抗网络的红外与可见光图像融合方法。

IF 10.6 1区计算机科学

IEEE Transactions on Image Processing Pub Date : 2023-05-10 DOI: 10.1109/TIP.2023.3273451

Dongyu Rao, Tianyang Xu, Xiao-Jun Wu

{"title":"TGFuse: An Infrared and Visible Image Fusion Approach Based on Transformer and Generative Adversarial Network.","authors":"Dongyu Rao, Tianyang Xu, Xiao-Jun Wu","doi":"10.1109/TIP.2023.3273451","DOIUrl":"10.1109/TIP.2023.3273451","url":null,"abstract":"<p><p>The end-to-end image fusion framework has achieved promising performance, with dedicated convolutional networks aggregating the multi-modal local appearance. However, long-range dependencies are directly neglected in existing CNN fusion approaches, impeding balancing the entire image-level perception for complex scenario fusion. In this paper, therefore, we propose an infrared and visible image fusion algorithm based on the transformer module and adversarial learning. Inspired by the global interaction power, we use the transformer technique to learn the effective global fusion relations. In particular, shallow features extracted by CNN are interacted in the proposed transformer fusion module to refine the fusion relationship within the spatial scope and across channels simultaneously. Besides, adversarial learning is designed in the training process to improve the output discrimination via imposing competitive consistency from the inputs, reflecting the specific characteristics in infrared and visible images. The experimental performance demonstrates the effectiveness of the proposed modules, with superior improvement against the state-of-the-art, generalising a novel paradigm via transformer and adversarial learning in the fusion task.</p>","PeriodicalId":13217,"journal":{"name":"IEEE Transactions on Image Processing","volume":"PP ","pages":""},"PeriodicalIF":10.6,"publicationDate":"2023-05-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9443051","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

DVMark: A Deep Multiscale Framework for Video Watermarking. DVMark：用于视频水印的深度多尺度框架。

IF 10.6 1区计算机科学

IEEE Transactions on Image Processing Pub Date : 2023-03-28 DOI: 10.1109/TIP.2023.3251737

Xiyang Luo, Yinxiao Li, Huiwen Chang, Ce Liu, Peyman Milanfar, Feng Yang

引用次数: 0

Rain Removal From Light Field Images With 4D Convolution and Multi-Scale Gaussian Process 基于4D卷积和多尺度高斯过程的光场图像去雨

IF 10.6 1区计算机科学

IEEE Transactions on Image Processing Pub Date : 2022-08-16 DOI: 10.1109/TAP.2022.3218759

Zhiqiang Yuan, Jianhua Zhang, Yilin Ji, G. Pedersen, W. Fan

{"title":"Rain Removal From Light Field Images With 4D Convolution and Multi-Scale Gaussian Process","authors":"Zhiqiang Yuan, Jianhua Zhang, Yilin Ji, G. Pedersen, W. Fan","doi":"10.1109/TAP.2022.3218759","DOIUrl":"https://doi.org/10.1109/TAP.2022.3218759","url":null,"abstract":"Existing deraining methods focus mainly on a single input image. However, with just a single input image, it is extremely difficult to accurately detect and remove rain streaks, in order to restore a rain-free image. In contrast, a light field image (LFI) embeds abundant 3D structure and texture information of the target scene by recording the direction and position of each incident ray via a plenoptic camera. LFIs are becoming popular in the computer vision and graphics communities. However, making full use of the abundant information available from LFIs, such as 2D array of sub-views and the disparity map of each sub-view, for effective rain removal is still a challenging problem. In this paper, we propose a novel method, 4D-MGP-SRRNet, for rain streak removal from LFIs. Our method takes as input all sub-views of a rainy LFI. To make full use of the LFI, it adopts 4D convolutional layers to simultaneously process all sub-views of the LFI. In the pipeline, the rain detection network, MGPDNet, with a novel Multi-scale Self-guided Gaussian Process (MSGP) module is proposed to detect high-resolution rain streaks from all sub-views of the input LFI at multi-scales. Semi-supervised learning is introduced for MSGP to accurately detect rain streaks by training on both virtual-world rainy LFIs and real-world rainy LFIs at multi-scales via computing pseudo ground truths for real-world rain streaks. We then feed all sub-views subtracting the predicted rain streaks into a 4D convolution-based Depth Estimation Residual Network (DERNet) to estimate the depth maps, which are later converted into fog maps. Finally, all sub-views concatenated with the corresponding rain streaks and fog maps are fed into a powerful rainy LFI restoring model based on the adversarial recurrent neural network to progressively eliminate rain streaks and recover the rain-free LFI. Extensive quantitative and qualitative evaluations conducted on both synthetic LFIs and real-world LFIs demonstrate the effectiveness of our proposed method.","PeriodicalId":13217,"journal":{"name":"IEEE Transactions on Image Processing","volume":"32 1","pages":"921-936"},"PeriodicalIF":10.6,"publicationDate":"2022-08-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48830864","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: 5

Designing an Illumination-Aware Network for Deep Image Relighting 设计一种用于深度图像重照明的照明感知网络

IF 10.6 1区计算机科学

IEEE Transactions on Image Processing Pub Date : 2022-07-21 DOI: 10.48550/arXiv.2207.10582

Zuo-Liang Zhu, Z. Li, Ruimao Zhang, Chunle Guo, Ming-Ming Cheng

{"title":"Designing an Illumination-Aware Network for Deep Image Relighting","authors":"Zuo-Liang Zhu, Z. Li, Ruimao Zhang, Chunle Guo, Ming-Ming Cheng","doi":"10.48550/arXiv.2207.10582","DOIUrl":"https://doi.org/10.48550/arXiv.2207.10582","url":null,"abstract":"Lighting is a determining factor in photography that affects the style, expression of emotion, and even quality of images. Creating or finding satisfying lighting conditions, in reality, is laborious and time-consuming, so it is of great value to develop a technology to manipulate illumination in an image as post-processing. Although previous works have explored techniques based on the physical viewpoint for relighting images, extensive supervisions and prior knowledge are necessary to generate reasonable images, restricting the generalization ability of these works. In contrast, we take the viewpoint of image-to-image translation and implicitly merge ideas of the conventional physical viewpoint. In this paper, we present an Illumination-Aware Network (IAN) which follows the guidance from hierarchical sampling to progressively relight a scene from a single image with high efficiency. In addition, an Illumination-Aware Residual Block (IARB) is designed to approximate the physical rendering process and to extract precise descriptors of light sources for further manipulations. We also introduce a depth-guided geometry encoder for acquiring valuable geometry- and structure-related representations once the depth information is available. Experimental results show that our proposed method produces better quantitative and qualitative relighting results than previous state-of-the-art methods. The code and models are publicly available on https://github.com/NK-CS-ZZL/IAN.","PeriodicalId":13217,"journal":{"name":"IEEE Transactions on Image Processing","volume":"31 1","pages":"5396-5411"},"PeriodicalIF":10.6,"publicationDate":"2022-07-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49347222","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: 6