IEEE Transactions on Image Processing最新文献_第9页

Exploiting Block-sparsity for Hyperspectral Kronecker Compressive Sensing: a Tensor-based Bayesian Method. 利用块稀疏性实现高光谱克朗克尔压缩传感：基于张量的贝叶斯方法

IF 10.6 1区计算机科学

IEEE Transactions on Image Processing Pub Date : 2019-10-07 DOI: 10.1109/TIP.2019.2944722

Rongqiang Zhao, Qiang Wang, Jun Fu, Luquan Ren

{"title":"Exploiting Block-sparsity for Hyperspectral Kronecker Compressive Sensing: a Tensor-based Bayesian Method.","authors":"Rongqiang Zhao, Qiang Wang, Jun Fu, Luquan Ren","doi":"10.1109/TIP.2019.2944722","DOIUrl":"10.1109/TIP.2019.2944722","url":null,"abstract":"Bayesian methods are attracting increasing attention in the field of compressive sensing (CS), as they are applicable to recover signals from random measurements. However, these methods have limited use in many tensor-based cases such as hyperspectral Kronecker compressive sensing (HKCS), because they exploit the sparsity in only one dimension. In this paper, we propose a novel Bayesian model for HKCS in an attempt to overcome the above limitation. The model exploits multi-dimensional block-sparsity such that the information redundancies in all dimensions are eliminated. Laplace prior distributions are employed for sparse coefficients in each dimension, and their coupling is consistent with the multi-dimensional block-sparsity model. Based on the proposed model, we develop a tensor-based Bayesian reconstruction algorithm, which decouples the hyperparameters for each dimension via a low-complexity technique. Experimental results demonstrate that the proposed method is able to provide more accurate reconstruction than existing Bayesian methods at a satisfactory speed. Additionally, the proposed method can not only be used for HKCS, it also has the potential to be extended to other multi-dimensional CS applications and to multi-dimensional block-sparse-based data recovery.","PeriodicalId":13217,"journal":{"name":"IEEE Transactions on Image Processing","volume":"29 1","pages":""},"PeriodicalIF":10.6,"publicationDate":"2019-10-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"62590166","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

Skeleton Filter: A Self-Symmetric Filter for Skeletonization in Noisy Text Images. 骨架滤波器：用于噪声文本图像骨架化的自对称滤波器

IF 10.6 1区计算机科学

IEEE Transactions on Image Processing Pub Date : 2019-10-07 DOI: 10.1109/TIP.2019.2944560

Xiuxiu Bai, Lele Ye, Jihua Zhu, Li Zhu, Taku Komura

引用次数: 0

Deep Coupled ISTA Network for Multi-modal Image Super-Resolution. 用于多模态图像超分辨率的深度耦合 ISTA 网络

IF 10.6 1区计算机科学

IEEE Transactions on Image Processing Pub Date : 2019-10-03 DOI: 10.1109/TIP.2019.2944270

Xin Deng, Pier Luigi Dragotti

{"title":"Deep Coupled ISTA Network for Multi-modal Image Super-Resolution.","authors":"Xin Deng, Pier Luigi Dragotti","doi":"10.1109/TIP.2019.2944270","DOIUrl":"10.1109/TIP.2019.2944270","url":null,"abstract":"Given a low-resolution (LR) image, multi-modal image super-resolution (MISR) aims to find the high-resolution (HR) version of this image with the guidance of an HR image from another modality. In this paper, we use a model-based approach to design a new deep network architecture for MISR. We first introduce a novel joint multi-modal dictionary learning (JMDL) algorithm to model cross-modality dependency. In JMDL, we simultaneously learn three dictionaries and two transform matrices to combine the modalities. Then, by unfolding the iterative shrinkage and thresholding algorithm (ISTA), we turn the JMDL model into a deep neural network, called deep coupled ISTA network. Since the network initialization plays an important role in deep network training, we further propose a layer-wise optimization algorithm (LOA) to initialize the parameters of the network before running back-propagation strategy. Specifically, we model the network initialization as a multi-layer dictionary learning problem, and solve it through convex optimization. The proposed LOA is demonstrated to effectively decrease the training loss and increase the reconstruction accuracy. Finally, we compare our method with other state-of-the-art methods in the MISR task. The numerical results show that our method consistently outperforms others both quantitatively and qualitatively at different upscaling factors for various multi-modal scenarios.","PeriodicalId":13217,"journal":{"name":"IEEE Transactions on Image Processing","volume":"29 1","pages":""},"PeriodicalIF":10.6,"publicationDate":"2019-10-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"62589478","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

Semi-Supervised Human Detection via Region Proposal Networks Aided by Verification. 通过验证辅助区域建议网络进行半监督式人体检测

IF 10.6 1区计算机科学

IEEE Transactions on Image Processing Pub Date : 2019-10-03 DOI: 10.1109/TIP.2019.2944306

Si Wu, Wenhao Wu, Shiyao Lei, Sihao Lin, Rui Li, Zhiwen Yu, Hau-San Wong

{"title":"Semi-Supervised Human Detection via Region Proposal Networks Aided by Verification.","authors":"Si Wu, Wenhao Wu, Shiyao Lei, Sihao Lin, Rui Li, Zhiwen Yu, Hau-San Wong","doi":"10.1109/TIP.2019.2944306","DOIUrl":"10.1109/TIP.2019.2944306","url":null,"abstract":"In this paper, we explore how to leverage readily available unlabeled data to improve semi-supervised human detection performance. For this purpose, we specifically modify the region proposal network (RPN) for learning on a partially labeled dataset. Based on commonly observed false positive types, a verification module is developed to assess foreground human objects in the candidate regions to provide an important cue for filtering the RPN's proposals. The remaining proposals with high confidence scores are then used as pseudo annotations for re-training our detection model. To reduce the risk of error propagation in the training process, we adopt a self-paced training strategy to progressively include more pseudo annotations generated by the previous model over multiple training rounds. The resulting detector re-trained on the augmented data can be expected to have better detection performance. The effectiveness of the main components of this framework is verified through extensive experiments, and the proposed approach achieves state-of-the-art detection results on multiple scene-specific human detection benchmarks in the semi-supervised setting.","PeriodicalId":13217,"journal":{"name":"IEEE Transactions on Image Processing","volume":"29 1","pages":""},"PeriodicalIF":10.6,"publicationDate":"2019-10-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"62589764","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

Learning Interleaved Cascade of Shrinkage Fields for Joint Image Dehazing and Denoising. 为联合图像去污和去噪学习交错级联收缩场

IF 10.6 1区计算机科学

IEEE Transactions on Image Processing Pub Date : 2019-09-30 DOI: 10.1109/TIP.2019.2942504

Qingbo Wu, Wenqi Ren, Xiaochun Cao

引用次数: 0

A Biological Vision Inspired Framework for Image Enhancement in Poor Visibility Conditions. 在能见度较低的条件下增强图像的生物视觉启发框架。

IF 10.6 1区计算机科学

IEEE Transactions on Image Processing Pub Date : 2019-09-25 DOI: 10.1109/TIP.2019.2938310

Kai-Fu Yang, Xian-Shi Zhang, Yong-Jie Li

{"title":"A Biological Vision Inspired Framework for Image Enhancement in Poor Visibility Conditions.","authors":"Kai-Fu Yang, Xian-Shi Zhang, Yong-Jie Li","doi":"10.1109/TIP.2019.2938310","DOIUrl":"10.1109/TIP.2019.2938310","url":null,"abstract":"Image enhancement is an important pre-processing step for many computer vision applications especially regarding the scenes in poor visibility conditions. In this work, we develop a unified two-pathway model inspired by the biological vision, especially the early visual mechanisms, which contributes to image enhancement tasks including low dynamic range (LDR) image enhancement and high dynamic range (HDR) image tone mapping. Firstly, the input image is separated and sent into two visual pathways: structure-pathway and detail-pathway, corresponding to the M-and P-pathway in the early visual system, which code the low-and high-frequency visual information, respectively. In the structure-pathway, an extended biological normalization model is used to integrate the global and local luminance adaptation, which can handle the visual scenes with varying illuminations. On the other hand, the detail enhancement and local noise suppression are achieved in the detail-pathway based on local energy weighting. Finally, the outputs of structure-and detail-pathway are integrated to achieve the low-light image enhancement. In addition, the proposed model can also be used for tone mapping of HDR images with some fine-tuning steps. Extensive experiments on three datasets (two LDR image datasets and one HDR scene dataset) show that the proposed model can handle the visual enhancement tasks mentioned above efficiently and outperform the related state-of-the-art methods.","PeriodicalId":13217,"journal":{"name":"IEEE Transactions on Image Processing","volume":"29 1","pages":""},"PeriodicalIF":10.6,"publicationDate":"2019-09-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"62586368","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

Receptive Field Size vs. Model Depth for Single Image Super-resolution. 单张图像超分辨率的感受野大小与模型深度的关系

IF 10.6 1区计算机科学

IEEE Transactions on Image Processing Pub Date : 2019-09-25 DOI: 10.1109/TIP.2019.2941327

Ruxin Wang, Mingming Gong, Dacheng Tao

引用次数: 0

Intermediate Deep Feature Compression: Toward Intelligent Sensing. 中间深度特征压缩：迈向智能传感

IF 10.6 1区计算机科学

IEEE Transactions on Image Processing Pub Date : 2019-09-25 DOI: 10.1109/TIP.2019.2941660

Zhuo Chen, Kui Fan, Shiqi Wang, Lingyu Duan, Weisi Lin, Alex C Kot

{"title":"Intermediate Deep Feature Compression: Toward Intelligent Sensing.","authors":"Zhuo Chen, Kui Fan, Shiqi Wang, Lingyu Duan, Weisi Lin, Alex C Kot","doi":"10.1109/TIP.2019.2941660","DOIUrl":"10.1109/TIP.2019.2941660","url":null,"abstract":"The recent advances of hardware technology have made the intelligent analysis equipped at the front-end with deep learning more prevailing and practical. To better enable the intelligent sensing at the front-end, instead of compressing and transmitting visual signals or the ultimately utilized top-layer deep learning features, we propose to compactly represent and convey the intermediate-layer deep learning features with high generalization capability, to facilitate the collaborating approach between front and cloud ends. This strategy enables a good balance among the computational load, transmission load and the generalization ability for cloud servers when deploying the deep neural networks for large scale cloud based visual analysis. Moreover, the presented strategy also makes the standardization of deep feature coding more feasible and promising, as a series of tasks can simultaneously benefit from the transmitted intermediate layer features. We also present the results for evaluations of both lossless and lossy deep feature compression, which provide meaningful investigations and baselines for future research and standardization activities.","PeriodicalId":13217,"journal":{"name":"IEEE Transactions on Image Processing","volume":"29 1","pages":""},"PeriodicalIF":10.6,"publicationDate":"2019-09-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"62587991","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

Towards weakly-supervised focus region detection via recurrent constraint network. 通过递归约束网络实现弱监督焦点区域检测

IF 10.6 1区计算机科学

IEEE Transactions on Image Processing Pub Date : 2019-09-25 DOI: 10.1109/TIP.2019.2942505

Wenda Zhao, Xueqing Hou, Xiaobing Yu, You He, Huchuan Lu

{"title":"Towards weakly-supervised focus region detection via recurrent constraint network.","authors":"Wenda Zhao, Xueqing Hou, Xiaobing Yu, You He, Huchuan Lu","doi":"10.1109/TIP.2019.2942505","DOIUrl":"10.1109/TIP.2019.2942505","url":null,"abstract":"Recent state-of-the-art methods on focus region detection (FRD) rely on deep convolutional networks trained with costly pixel-level annotations. In this study, we propose a FRD method that achieves competitive accuracies but only uses easily obtained bounding box annotations. Box-level tags provide important cues of focus regions but lose the boundary delineation of the transition area. A recurrent constraint network (RCN) is introduced for this challenge. In our static training, RCN is jointly trained with a fully convolutional network (FCN) through box-level supervision. The RCN can generate a detailed focus map to locate the boundary of the transition area effectively. In our dynamic training, we iterate between fine-tuning FCN and RCN with the generated pixel-level tags and generate finer new pixel-level tags. To boost the performance further, a guided conditional random field is developed to improve the quality of the generated pixel-level tags. To promote further study of the weakly supervised FRD methods, we construct a new dataset called FocusBox, which consists of 5000 challenging images with bounding box-level labels. Experimental results on existing datasets demonstrate that our method not only yields comparable results than fully supervised counterparts but also achieves a faster speed.","PeriodicalId":13217,"journal":{"name":"IEEE Transactions on Image Processing","volume":"29 1","pages":""},"PeriodicalIF":10.6,"publicationDate":"2019-09-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"62588586","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

Deep Spatial and Temporal Network for Robust Visual Object Tracking. 用于稳健视觉对象跟踪的深度时空网络

IF 10.6 1区计算机科学

IEEE Transactions on Image Processing Pub Date : 2019-09-25 DOI: 10.1109/TIP.2019.2942502

Zhu Teng, Junliang Xing, Qiang Wang, Baopeng Zhang, Jianping Fan

{"title":"Deep Spatial and Temporal Network for Robust Visual Object Tracking.","authors":"Zhu Teng, Junliang Xing, Qiang Wang, Baopeng Zhang, Jianping Fan","doi":"10.1109/TIP.2019.2942502","DOIUrl":"10.1109/TIP.2019.2942502","url":null,"abstract":"There are two key components that can be leveraged for visual tracking: (a) object appearances; and (b) object motions. Many existing techniques have recently employed deep learning to enhance visual tracking due to its superior representation power and strong learning ability, where most of them employed object appearances but few of them exploited object motions. In this work, a deep spatial and temporal network (DSTN) is developed for visual tracking by explicitly exploiting both the object representations from each frame and their dynamics along multiple frames in a video, such that it can seamlessly integrate the object appearances with their motions to produce compact object appearances and capture their temporal variations effectively. Our DSTN method, which is deployed into a tracking pipeline in a coarse-to-fine form, can perceive the subtle differences on spatial and temporal variations of the target (object being tracked), and thus it benefits from both off-line training and online fine-tuning. We have also conducted our experiments over four largest tracking benchmarks, including OTB-2013, OTB-2015, VOT2015, and VOT2017, and our experimental results have demonstrated that our DSTN method can achieve competitive performance as compared with the state-of-the-art techniques. The source code, trained models, and all the experimental results of this work will be made public available to facilitate further studies on this problem.","PeriodicalId":13217,"journal":{"name":"IEEE Transactions on Image Processing","volume":"29 1","pages":""},"PeriodicalIF":10.6,"publicationDate":"2019-09-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"62588807","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