The IEEE Transactions on Image Processing covers novel theory, algorithms, and architectures for the formation, capture, processing, communication, analysis, and display of images, video, and multidimensional signals in a wide variety of applications. Topics of interest include, but are not limited to, the mathematical, statistical, and perceptual modeling, representation, formation, coding, filtering, enhancement, restoration, rendering, halftoning, search, and analysis of images, video, and multidimensional signals. Applications of interest include image and video communications, electronic imaging, biomedical imaging, image and video systems, and remote sensing. Indexed in Pubmed® and Medline®, products of the United States National Laboratory of Medicine
IEEE Transactions on Image Processing
Description
Identifiers
ISSN | 1057-7149 |
e-ISSN | 1941-0042 |
Publisher
IEEE
Additional information
Data set: ieee
Articles
IEEE Transactions on Image Processing > 2018 > 27 > 3 > 1311 - 1322
In recent saliency detection research, many graph-based algorithms have applied boundary priors as background queries, which may generate completely “reversed” saliency maps if the salient objects are on the image boundaries. Moreover, these algorithms usually depend heavily on pre-processed superpixel segmentation, which may lead to notable degradation in image detail features. In this paper, a novel...
IEEE Transactions on Image Processing > 2018 > 27 > 3 > 1152 - 1163
Recently, a tensor nuclear norm (TNN) based method was proposed to solve the tensor completion problem, which has achieved state-of-the-art performance on image and video inpainting tasks. However, it requires computing tensor singular value decomposition (t-SVD), which costs much computation and thus cannot efficiently handle tensor data, due to its natural large scale. Motivated by TNN, we propose...
IEEE Transactions on Image Processing > 2018 > 27 > 3 > 1060 - 1075
Since a light-field camera is able to capture more information than a traditional camera, a lot of methods, such as depth estimation, image super-resolution, and view synthesis, are explored for recovering scene information. In this paper, we propose a novel framework for scene recovery based on lenslet-based light-field camera images. Instead of using traditional matching terms, we design a new micro-lens-based...