IEEE Transactions on Image Processing最新文献_第10页

Digital affine shear filter banks with 2-layer structure 两层结构的数字仿射剪切滤波器组

IF 10.6 1区计算机科学

IEEE Transactions on Image Processing Pub Date : 2017-07-01 DOI: 10.1109/SAMPTA.2017.8024369

Zhihua Che, X. Zhuang

引用次数: 6

Improved Denoising via Poisson Mixture Modeling of Image Sensor Noise 基于泊松混合模型的图像传感器噪声改进去噪

IF 10.6 1区计算机科学

IEEE Transactions on Image Processing Pub Date : 2017-04-01 DOI: 10.1109/TIP.2017.2651365

Jiachao Zhang, Keigo Hirakawa

引用次数: 32

Non-Additive Imprecise Image Super-Resolution in a Semi-Blind Context 半盲环境下的非加性不精确图像超分辨率

IF 10.6 1区计算机科学

IEEE Transactions on Image Processing Pub Date : 2017-03-01 DOI: 10.1109/TIP.2016.2621414

Fares Graba, F. Comby, O. Strauss

引用次数: 9

Bayesian Contrast Measures and Clutter Distribution Determinants of Human Target Detection 人体目标检测中的贝叶斯对比度量和杂波分布决定因素

IF 10.6 1区计算机科学

IEEE Transactions on Image Processing Pub Date : 2017-03-01 DOI: 10.1109/TIP.2016.2644269

A. Novak, N. Armstrong, T. Caelli, Iain Blair

{"title":"Bayesian Contrast Measures and Clutter Distribution Determinants of Human Target Detection","authors":"A. Novak, N. Armstrong, T. Caelli, Iain Blair","doi":"10.1109/TIP.2016.2644269","DOIUrl":"https://doi.org/10.1109/TIP.2016.2644269","url":null,"abstract":"Human target detection is known to be dependent on a number of components: one, basic electro-optics including image contrast, the target size, pixel resolution, and contrast sensitivity; two, target shape, image type and features, types of clutter; and three, context and task requirements. Here, we consider a Bayesian approach to investigating how these components contribute to target detection. To this end, we develop and compare three different formulations for contrast: mean contrast, perceptual contrast, and a Bayesian-based histogram contrast statistic. Results on past detection data show how the latter contrast measure correlates well with human performance factoring out all other dimensions. As for clutter, our findings show that with large targets, there are effectively no clutter effects. Furthermore, clutter does not have a major effect on detection when it is not contiguous with the target even when it is smaller. However, except for large targets, when the target is contiguous with the clutter, detection clearly decreases as a function of the similarity of target and clutter features—creating type of “clutter camouflage”. This Bayesian formulation uses priors based on the contrast histogram statistics derived from all the images, the image context, and implies that human observers have adapted their criteria to fit with the image set, context, and task.","PeriodicalId":13217,"journal":{"name":"IEEE Transactions on Image Processing","volume":"26 1","pages":"1115-1126"},"PeriodicalIF":10.6,"publicationDate":"2017-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1109/TIP.2016.2644269","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"62581026","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: 2

Waterloo Exploration Database: New Challenges for Image Quality Assessment Models 滑铁卢探索数据库:图像质量评估模型的新挑战

IF 10.6 1区计算机科学

IEEE Transactions on Image Processing Pub Date : 2017-02-01 DOI: 10.1109/TIP.2016.2631888

Kede Ma, Zhengfang Duanmu, Q. Wu, Zhou Wang, Hongwei Yong, Hongliang Li, Lei Zhang

{"title":"Waterloo Exploration Database: New Challenges for Image Quality Assessment Models","authors":"Kede Ma, Zhengfang Duanmu, Q. Wu, Zhou Wang, Hongwei Yong, Hongliang Li, Lei Zhang","doi":"10.1109/TIP.2016.2631888","DOIUrl":"https://doi.org/10.1109/TIP.2016.2631888","url":null,"abstract":"The great content diversity of real-world digital images poses a grand challenge to image quality assessment (IQA) models, which are traditionally designed and validated on a handful of commonly used IQA databases with very limited content variation. To test the generalization capability and to facilitate the wide usage of IQA techniques in real-world applications, we establish a large-scale database named the Waterloo Exploration Database, which in its current state contains 4744 pristine natural images and 94 880 distorted images created from them. Instead of collecting the mean opinion score for each image via subjective testing, which is extremely difficult if not impossible, we present three alternative test criteria to evaluate the performance of IQA models, namely, the pristine/distorted image discriminability test, the listwise ranking consistency test, and the pairwise preference consistency test (P-test). We compare 20 well-known IQA models using the proposed criteria, which not only provide a stronger test in a more challenging testing environment for existing models, but also demonstrate the additional benefits of using the proposed database. For example, in the P-test, even for the best performing no-reference IQA model, more than 6 million failure cases against the model are “discovered” automatically out of over 1 billion test pairs. Furthermore, we discuss how the new database may be exploited using innovative approaches in the future, to reveal the weaknesses of existing IQA models, to provide insights on how to improve the models, and to shed light on how the next-generation IQA models may be developed. The database and codes are made publicly available at: https://ece.uwaterloo.ca/~k29ma/exploration/.","PeriodicalId":13217,"journal":{"name":"IEEE Transactions on Image Processing","volume":"26 1","pages":"1004-1016"},"PeriodicalIF":10.6,"publicationDate":"2017-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1109/TIP.2016.2631888","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"62578720","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: 459

A New Intrinsic-Lighting Color Space for Daytime Outdoor Images 一种新的白天户外图像的内在照明色彩空间

IF 10.6 1区计算机科学

IEEE Transactions on Image Processing Pub Date : 2017-02-01 DOI: 10.1109/TIP.2016.2642788

Zhi Han, Jiandong Tian, Liangqiong Qu, Yandong Tang

引用次数: 6

ORGM: Occlusion Relational Graphical Model for Human Pose Estimation 人体姿态估计的遮挡关系图形模型

IF 10.6 1区计算机科学

IEEE Transactions on Image Processing Pub Date : 2017-02-01 DOI: 10.1109/TIP.2016.2639441

Lianrui Fu, Junge Zhang, Kaiqi Huang

引用次数: 20

Removal of Canvas Patterns in Digital Acquisitions of Paintings 数字绘画收购中画布图案的去除

IF 10.6 1区计算机科学

IEEE Transactions on Image Processing Pub Date : 2017-01-01 DOI: 10.1109/TIP.2016.2621413

Bruno Cornelis, Haizhao Yang, Alex Goodfriend, Noelle Ocon, Jianfeng Lu, I. Daubechies

{"title":"Removal of Canvas Patterns in Digital Acquisitions of Paintings","authors":"Bruno Cornelis, Haizhao Yang, Alex Goodfriend, Noelle Ocon, Jianfeng Lu, I. Daubechies","doi":"10.1109/TIP.2016.2621413","DOIUrl":"https://doi.org/10.1109/TIP.2016.2621413","url":null,"abstract":"We address the removal of canvas artifacts from high-resolution digital photographs and X-ray images of paintings on canvas. Both imaging modalities are common investigative tools in art history and art conservation. Canvas artifacts manifest themselves very differently according to the acquisition modality; they can hamper the visual reading of the painting by art experts, for instance, in preparing a restoration campaign. Computer-aided canvas removal is desirable for restorers when the painting on canvas they are preparing to restore has acquired over the years a much more salient texture. We propose a new algorithm that combines a cartoon-texture decomposition method with adaptive multiscale thresholding in the frequency domain to isolate and suppress the canvas components. To illustrate the strength of the proposed method, we provide various examples, for acquisitions in both imaging modalities, for paintings with different types of canvas and from different periods. The proposed algorithm outperforms previous methods proposed for visual photographs such as morphological component analysis and Wiener filtering and it also works for the digital removal of canvas artifacts in X-ray images.","PeriodicalId":13217,"journal":{"name":"IEEE Transactions on Image Processing","volume":"26 1","pages":"160-171"},"PeriodicalIF":10.6,"publicationDate":"2017-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1109/TIP.2016.2621413","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"62575511","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: 24

Fast Bilateral Filtering for Denoising Large 3D Images 快速双边滤波去噪大型3D图像

IF 10.6 1区计算机科学

IEEE Transactions on Image Processing Pub Date : 2017-01-01 DOI: 10.1109/TIP.2016.2624148

G. Papari, N. Idowu, T. Varslot

引用次数: 51

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