IEEE Transactions on Image Processing最新文献

Pro2Diff: Proposal Propagation for Multi-Object Tracking via the Diffusion Model Pro2Diff：通过扩散模型进行多目标跟踪的提案传播

IF 10.6 1区计算机科学

IEEE Transactions on Image Processing Pub Date : 2024-11-14 DOI: 10.1109/tip.2024.3494600

Hongmin Liu, Canbin Zhang, Bin Fan, Jinglin Xu

引用次数: 0

Enhanced Multispectral Band-to-Band Registration using Co-occurrence Scale Space and Spatial Confined RANSAC Guided Segmented Affine Transformation 利用共现尺度空间和空间限制 RANSAC 引导的分段仿射变换增强多光谱波段到波段的配准

IF 10.6 1区计算机科学

IEEE Transactions on Image Processing Pub Date : 2024-11-14 DOI: 10.1109/tip.2024.3494555

Indranil Misra, Mukesh Kumar Rohil, S. Manthira Moorthi, Debajyoti Dhar

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

Towards Transparent Deep Image Aesthetics Assessment with Tag-based Content Descriptors. 利用基于标签的内容描述符实现透明的深度图像美学评估

IF 10.6 1区计算机科学

IEEE Transactions on Image Processing Pub Date : 2023-08-30 DOI: 10.1109/TIP.2023.3308852

Jingwen Hou, Weisi Lin, Yuming Fang, Haoning Wu, Chaofeng Chen, Liang Liao, Weide Liu

{"title":"Towards Transparent Deep Image Aesthetics Assessment with Tag-based Content Descriptors.","authors":"Jingwen Hou, Weisi Lin, Yuming Fang, Haoning Wu, Chaofeng Chen, Liang Liao, Weide Liu","doi":"10.1109/TIP.2023.3308852","DOIUrl":"10.1109/TIP.2023.3308852","url":null,"abstract":"Deep learning approaches for Image Aesthetics Assessment (IAA) have shown promising results in recent years, but the internal mechanisms of these models remain unclear. Previous studies have demonstrated that image aesthetics can be predicted using semantic features, such as pre-trained object classification features. However, these semantic features are learned implicitly, and therefore, previous works have not elucidated what the semantic features are representing. In this work, we aim to create a more transparent deep learning framework for IAA by introducing explainable semantic features. To achieve this, we propose Tag-based Content Descriptors (TCDs), where each value in a TCD describes the relevance of an image to a human-readable tag that refers to a specific type of image content. This allows us to build IAA models from explicit descriptions of image contents. We first propose the explicit matching process to produce TCDs that adopt predefined tags to describe image contents. We show that a simple MLP-based IAA model with TCDs only based on predefined tags can achieve an SRCC of 0.767, which is comparable to most state-of-the-art methods. However, predefined tags may not be sufficient to describe all possible image contents that the model may encounter. Therefore, we further propose the implicit matching process to describe image contents that cannot be described by predefined tags. By integrating components obtained from the implicit matching process into TCDs, the IAA model further achieves an SRCC of 0.817, which significantly outperforms existing IAA methods. Both the explicit matching process and the implicit matching process are realized by the proposed TCD generator. To evaluate the performance of the proposed TCD generator in matching images with predefined tags, we also labeled 5101 images with photography-related tags to form a validation set. And experimental results show that the proposed TCD generator can meaningfully assign photography-related tags to images.","PeriodicalId":13217,"journal":{"name":"IEEE Transactions on Image Processing","volume":"PP ","pages":""},"PeriodicalIF":10.6,"publicationDate":"2023-08-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10207498","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

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":"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.","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

USOD10K: A New Benchmark Dataset for Underwater Salient Object Detection. USOD10K：水下突出物体检测的新基准数据集。

IF 10.6 1区计算机科学

IEEE Transactions on Image Processing Pub Date : 2023-04-14 DOI: 10.1109/TIP.2023.3266163

Lin Hong, Xin Wang, Gan Zhang, Ming Zhao

{"title":"USOD10K: A New Benchmark Dataset for Underwater Salient Object Detection.","authors":"Lin Hong, Xin Wang, Gan Zhang, Ming Zhao","doi":"10.1109/TIP.2023.3266163","DOIUrl":"10.1109/TIP.2023.3266163","url":null,"abstract":"Underwater salient object detection (USOD) attracts increasing interest for its promising performance in various underwater visual tasks. However, USOD research is still in its early stages due to the lack of large-scale datasets within which salient objects are well-defined and pixel-wise annotated. To address this issue, this paper introduces a new dataset named USOD10K. It consists of 10,255 underwater images, covering 70 categories of salient objects in 12 different underwater scenes. In addition, salient object boundaries and depth maps of all images are provided in this dataset. The USOD10K is the first large-scale dataset in the USOD community, making a significant leap in diversity, complexity, and scalability. Secondly, a simple but strong baseline termed TC-USOD is designed for the USOD10K. The TC-USOD adopts a hybrid architecture based on an encoder-decoder design that leverages transformer and convolution as the basic computational building block of the encoder and decoder, respectively. Thirdly, we make a comprehensive summarization of 35 cutting-edge SOD/USOD methods and benchmark them over the existing USOD dataset and the USOD10K. The results show that our TC-USOD obtained superior performance on all datasets tested. Finally, several other use cases of the USOD10K are discussed, and future directions of USOD research are pointed out. This work will promote the development of the USOD research and facilitate further research on underwater visual tasks and visually-guided underwater robots. To pave the road in this research field, all the dataset, code, and benchmark results are publicly available: https://github.com/LinHong-HIT/USOD10K.","PeriodicalId":13217,"journal":{"name":"IEEE Transactions on Image Processing","volume":"PP ","pages":""},"PeriodicalIF":10.6,"publicationDate":"2023-04-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9781338","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

Radiometric Compensation of Images Projected on Non-White Surfaces by Exploiting Chromatic Adaptation and Perceptual Anchoring. 利用色彩适应和感知锚定的非白色表面投影图像的辐射补偿。

IF 10.6 1区计算机科学

IEEE Transactions on Image Processing Pub Date : 2017-01-01 Epub Date: 2016-07-18 DOI: 10.1109/TIP.2016.2592799

Tai-Hsiang Huang, Ting-Chun Wang, Homer H Chen

引用次数: 7

Incorporating Spatial Information and Endmember Variability Into Unmixing Analyses to Improve Abundance Estimates. 将空间信息和端元变异性纳入分离分析以改进丰度估算。

IF 10.6 1区计算机科学

IEEE Transactions on Image Processing Pub Date : 2016-12-01 Epub Date: 2016-08-18 DOI: 10.1109/TIP.2016.2601269

Tatsumi Uezato, Richard J Murphy, Arman Melkumyan, Anna Chlingaryan

{"title":"Incorporating Spatial Information and Endmember Variability Into Unmixing Analyses to Improve Abundance Estimates.","authors":"Tatsumi Uezato, Richard J Murphy, Arman Melkumyan, Anna Chlingaryan","doi":"10.1109/TIP.2016.2601269","DOIUrl":"https://doi.org/10.1109/TIP.2016.2601269","url":null,"abstract":"Incorporating endmember variability and spatial information into spectral unmixing analyses is important for producing accurate abundance estimates. However, most methods do not incorporate endmember variability with spatial regularization. This paper proposes a novel 2-step unmixing approach, which incorporates endmember variability and spatial information. In step 1, a probability distribution representing abundances is estimated by spectral unmixing within a multi-task Gaussian process framework (SUGP). In step 2, spatial information is incorporated into the probability distribution derived by SUGP through an a priori distribution derived from a Markov random field (MRF). The proposed method (SUGP-MRF) is different to the existing unmixing methods because it incorporates endmember variability and spatial information at separate steps in the analysis and automatically estimates parameters controlling the balance between the data fit and spatial smoothness. The performance of SUGP-MRF is compared with the existing unmixing methods using synthetic imagery with precisely known abundances and real hyperspectral imagery of rock samples. Results show that SUGP-MRF outperforms the existing methods and improves the accuracy of abundance estimates by incorporating spatial information.","PeriodicalId":13217,"journal":{"name":"IEEE Transactions on Image Processing","volume":"25 12","pages":"5563-5575"},"PeriodicalIF":10.6,"publicationDate":"2016-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1109/TIP.2016.2601269","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"34686710","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: 20