Results 251 to 260 of about 360,556 (315)

Reconstructed Densenets for Image Super-Resolution

2018 25th IEEE International Conference on Image Processing (ICIP), 2018
Deep learning has been successfully applied to single image super-resolution problem due to its high data fitting ability. However, the trending of deeper layers and wider receptive field to acquire better performance brings high computation complexity and serious information vanishing. To address this problem, we proposed a new Reconstructed DenseNets
Lingfeng Wang 0002, Linwei Qiu, Wei Sui, Chunhong Pan   +3 more
openaire   +1 more source

Fluid Micelle Network for Image Super-Resolution Reconstruction

IEEE Transactions on Cybernetics, 2022
Most existing convolutional neural-network-based super-resolution (SR) methods focus on designing effective neural blocks but rarely describe the image SR mechanism from the perspective of image evolution in the SR process.
Mingjin Zhang, Qianqian Wu, Jing Zhang, Xinbo Gao, Jie-Ru Guo, Dacheng Tao   +5 more
semanticscholar   +1 more source

Super-resolution reconstruction of image sequences

IEEE Transactions on Pattern Analysis and Machine Intelligence, 1999
In an earlier work (1999), we introduced the problem of reconstructing a super-resolution image sequence from a given low resolution sequence. We proposed two iterative algorithms, the R-SD and the R-LMS, to generate the desired image sequence.
Michael Elad, Arie Feuer
openaire   +1 more source

Stochastic super-resolution image reconstruction

Journal of Visual Communication and Image Representation, 2010
The objective of super-resolution (SR) imaging is to reconstruct a single higher-resolution image based on a set of lower-resolution images that were acquired from the same scene to overcome the limitations of image acquisition process for facilitating better visualization and content recognition.
Jing Tian 0002, Kai-Kuang Ma
openaire   +1 more source

Heat Transfer-Inspired Network for Image Super-Resolution Reconstruction

IEEE Transactions on Neural Networks and Learning Systems, 2022
Image super-resolution (SR) is a critical image preprocessing task for many applications. How to recover features as accurately as possible is the focus of SR algorithms.
Mingjin Zhang, Qianqian Wu, Jie Guo, Yunsong Li, Xinbo Gao   +4 more
semanticscholar   +1 more source

Super‐resolution image reconstruction using multisensors

Numerical Linear Algebra with Applications, 2004
AbstractSuper‐resolution image reconstruction refers to obtaining an image at a resolution higher than that of a camera (sensor) used in recording the image. In this paper, we present a new joint minimization model in which an objective function is set up consisting of three terms: the data fitting term, the regularization terms for the reconstructed ...
Wai-Ki Ching, Michael K. Ng 0001, Kenton N. Sze, Andy C. Yau   +3 more
openaire   +3 more sources

Super-resolution image reconstruction

2010 International Conference on Computer Application and System Modeling (ICCASM 2010), 2010
Super-resolution image reconstruction is a technique to reconstruct high resolution image or video from a sequence of low resolution images. The super resolution method is summarized in this paper. The frequency domain method, non-uniform interpolation, POCS method, iterative back projection method, Bayesian approach, regularization method are both ...
null Xue-fen Wan, null Yi Yang
openaire   +1 more source

A novel fuzzy hierarchical fusion attention convolution neural network for medical image super-resolution reconstruction

Information Sciences, 2022
The clarity of medical images is crucial for doctors to identify and diagnose different diseases. High-resolution images have more detailed information and clearer content than low-resolution images.
Changzhong Wang, Xiangjie Lv, Mingwen Shao, Yuhua Qian, Yang Zhang   +4 more
semanticscholar   +1 more source

Super-resolution reconstruction of an image

Proceedings of 19th Convention of Electrical and Electronics Engineers in Israel, 2002
This paper presents a generalization of restoration theory for the problem of super-resolution reconstruction (SRR) of an image. In the SRR problem, a set of low quality images is given, and a single improved quality image which fuses their information is required.
M. Elad, A. Feuer
openaire   +1 more source

A Weighted and Combined Super Resolution Reconstruction Algorithm

2010 International Conference on E-Business and E-Government, 2010
The weaknesses between L1 norm minimization estimator and L2 norm minimization estimator in the traditional super resolution reconstruction problem are analyzed. In this paper, L1 norm and L2 norm are weighted and combined to measure the data fidelity term, and based on an approximate total variation regularization method [1][2], a robust weighted and ...
Mei Gong, Jiliu Zhou, Kun He 0007
openaire   +1 more source