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

Recalling Unknowns without Losing Precision: An Effective Solution to Large Model-Guided Open World Object Detection 在不损失精确度的情况下恢复未知数据：大型模型引导的开放世界物体检测的有效解决方案

IF 10.6 1区计算机科学

IEEE Transactions on Image Processing Pub Date : 2024-09-18 DOI: 10.1109/tip.2024.3459589

Yulin He, Wei Chen, Siqi Wang, Tianrui Liu, Meng Wang

引用次数: 0

HeightFormer: Explicit Height Modeling without Extra Data for Camera-only 3D Object Detection in Bird’s Eye View HeightFormer：明确的高度建模，无需额外数据，实现鸟瞰图中仅摄像头的 3D 物体检测

IF 10.6 1区计算机科学

IEEE Transactions on Image Processing Pub Date : 2024-09-09 DOI: 10.1109/tip.2024.3427701

Yiming Wu, Ruixiang Li, Zequn Qin, Xinhai Zhao, Xi Li

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

Relationship-Incremental Scene Graph Generation by a Divide-and-Conquer Pipeline with Feature Adapter 通过带有特征适配器的分而治之流水线生成关系递增场景图

IF 10.6 1区计算机科学

IEEE Transactions on Image Processing Pub Date : 2024-04-08 DOI: 10.1109/tip.2024.3384096

Xuewei Li, Guangcong Zheng, Yunlong Yu, Naye Ji, Xi Li

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

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