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A very lightweight image super-resolution network.

Sci Rep
Bai H, Liang X.
europepmc   +1 more source

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Image Super-Resolution Via Sparse Representation

IEEE Transactions on Image Processing, 2010
John Wright
exaly   +2 more sources

Exploiting Diffusion Prior for Real-World Image Super-Resolution

International Journal of Computer Vision, 2023
We present a novel approach to leverage prior knowledge encapsulated in pre-trained text-to-image diffusion models for blind super-resolution. Specifically, by employing our time-aware encoder, we can achieve promising restoration results without ...
Jianyi Wang, Zongsheng Yue, Shangchen Zhou, Kelvin C. K. Chan, Chen Change Loy   +4 more
semanticscholar   +1 more source

Image Super-Resolution Using Very Deep Residual Channel Attention Networks

European Conference on Computer Vision, 2018
Convolutional neural network (CNN) depth is of crucial importance for image super-resolution (SR). However, we observe that deeper networks for image SR are more difficult to train.
Yulun Zhang, Kunpeng Li, Kai Li, Lichen Wang, Bineng Zhong, Y. Fu   +5 more
semanticscholar   +1 more source

Super-resolution inducing of an image

Proceedings 1998 International Conference on Image Processing. ICIP98 (Cat. No.98CB36269), 2002
The problem of increasing the resolution of an image I/sub k/ is stated as an inverse problem of image reduction. The enlarged image must belong to the set of images which best approximates I/sub k/ after reducing. A projection of any image onto this set provides one of the possible enlarged images of I/sub k/.
Didier Calle, Annick Montanvert
openaire   +1 more source

Scalable Super-Resolution Imaging

2006 IEEE International Conference on Systems, Man and Cybernetics, 2006
In this study, we have developed a novel method to obtain high resolution images using an ordinary digital camera. Using successively taken images, our fusion algorithm has a profound effect in the quality of the image which contains richer independent pixels than the originals do.
Evrim Ozcelik, S. Murat Yesiloglu, Osman Kaan Erol, Hakan Temeltas, Okyay Kaynak   +4 more
openaire   +1 more source

Image Super-Resolution via Deep Recursive Residual Network

Computer Vision and Pattern Recognition, 2017
Recently, Convolutional Neural Network (CNN) based models have achieved great success in Single Image Super-Resolution (SISR). Owing to the strength of deep networks, these CNN models learn an effective nonlinear mapping from the low-resolution input ...
Ying Tai, Jian Yang, Xiaoming Liu
semanticscholar   +1 more source

Super-resolution in PET imaging

IEEE Transactions on Medical Imaging, 2006
This paper demonstrates a super-resolution method for improving the resolution in clinical positron emission tomography (PET) scanners. Super-resolution images were obtained by combining four data sets with spatial shifts between consecutive acquisitions and applying an iterative algorithm.
John A. Kennedy, Ora Israel, Alex Frenkel, Rachel Bar-Shalom, Haim Azhari   +4 more
openaire   +2 more sources

Super-Resolution Imaging and Plasmonics

Chemical Reviews, 2017
This review describes the growing partnership between super-resolution imaging and plasmonics, by describing the various ways in which the two topics mutually benefit one another to enhance our understanding of the nanoscale world. First, localization-based super-resolution imaging strategies, where molecules are modulated between emissive and ...
Katherine A. Willets, Andrew J. Wilson, Vignesh Sundaresan, Padmanabh B. Joshi   +3 more
openaire   +2 more sources

Second-Order Attention Network for Single Image Super-Resolution

Computer Vision and Pattern Recognition, 2019
Recently, deep convolutional neural networks (CNNs) have been widely explored in single image super-resolution (SISR) and obtained remarkable performance.
Tao Dai, Jianrui Cai, Yongbing Zhang, Shutao Xia, Lei Zhang   +4 more
semanticscholar   +1 more source