Results 261 to 270 of about 360,556 (315)

Deep-learning-based super-resolution reconstruction of high-speed imaging in fluids

The Physics of Fluids, 2022
In many fluid experiments, we can only obtain low-spatial high-temporal resolution flow images and high-spatial low-temporal resolution flow images due to the limitation of high-speed imaging systems.
Zhibo Wang, Xiangru Li, Luhan Liu, Xuecheng Wu, P. Hao, Xiwen Zhang, Feng He   +6 more
semanticscholar   +1 more source

Performance verification of super-resolution image reconstruction

2013 International Symposium on Intelligent Signal Processing and Communication Systems, 2013
In this paper, the specific limitation of super-resolution image reconstruction (SRR) is reported. In many SRR studies, specially made content was used, rather than general Television and cinema content, but the amount of aliasing differs between the two.
Masaki Sugie, Seiichi Gohshi
openaire   +1 more source

Research on Image Super-Resolution Reconstruction Mechanism based on Convolutional Neural Network

International Conference on Artificial Intelligence, Automation and High Performance Computing
Super-resolution reconstruction techniques entail the utilization of software algorithms to transform one or more sets of low-resolution images captured from the same scene into high-resolution images.
Hao Yan, Zixiang Wang, Zhengjia Xu, Zhuoyue Wang, Zhizhong Wu, Ranran Lyu   +5 more
semanticscholar   +1 more source

Super-resolution reconstruction of flow fields coupled with feature recognition

The Physics of Fluids
Traditional super-resolution reconstruction methods for flow fields use end-to-end mapping to determine the relationship between high- and low-resolution flow field data.
Fazhi Tang, Feng Liu, Anping Wu, Qingfeng Wang, Jun Huang, Yubo Li   +5 more
semanticscholar   +1 more source

Confidence map based super-resolution reconstruction

SPIE Proceedings, 2012
Magnetic Resonance Imaging and Computed Tomography usually provide highly anisotropic image data, so that the resolution in the slice-selection direction is poorer than in the in-plane directions. An isotropic high-resolution image can be reconstructed from two orthogonal scans of the same object.
Wissam El Hakimi, Stefan Wesarg
openaire   +1 more source

Deep learning methods for super-resolution reconstruction of turbulent flows

The Physics of Fluids, 2020
Two deep learning (DL) models addressing the super-resolution (SR) reconstruction of turbulent flows from low-resolution coarse flow field data are developed.
Bo Liu, Jiupeng Tang, Haibo Huang, Xi-yun Lu   +3 more
semanticscholar   +1 more source

Super resolution based on simultaneous registration and reconstruction

2008 10th International Conference on Control, Automation, Robotics and Vision, 2008
The progress of super-resolution usually involves computing the parameters (e.g. the registration and blur parameters) of a generating imaging model, and obtaining an estimated SR image by minimizing a cost function. In this paper, a high-resolution image, the registration parameters and PSF width will be optimized simultaneously using scaled conjugate
Zhi Yuan, Peimin Yan, Sheng Li, Juxia Shen, Yuhui Huang   +4 more
openaire   +1 more source

Regularization for Super-Resolution Image Reconstruction

2006
Super-resolution image reconstruction estimates a high-resolution image from a sequence of low-resolution, aliased images. The estimation is an inverse problem and is known to be ill-conditioned, in the sense that small errors in the observed images can cause large changes in the reconstruction.
openaire   +2 more sources

A super-resolution reconstruction algorithm for hyperspectral images

Signal Processing, 2012
The spatial resolution of a hyperspectral image is often coarse because of the limitations of the imaging hardware. Super-resolution reconstruction (SRR) is a promising signal post-processing technique for hyperspectral image resolution enhancement.
Hongyan Zhang 0001, Liangpei Zhang 0001, Huanfeng Shen   +2 more
openaire   +1 more source

CT image super-resolution reconstruction based on global hybrid attention

Comput. Biol. Medicine, 2022
Computer tomography (CT) has played an essential role in the field of medical diagnosis, but the blurry edges and unclear textures in traditional CT images usually interfere the subsequent judgement from radiologists or clinicians.
Jianning Chi, Zhiyi Sun, Huan Wang, Pengfei Lyu, Xiaosheng Yu, Chengdong Wu   +5 more
semanticscholar   +1 more source