Results 31 to 40 of about 1,135,244 (321)
Guided Cascaded Super-Resolution Network for Face Image
IEEE Access, 2020 The image super-resolution algorithm can overcome the imaging system's hardware limitation and obtain higher resolution and clearer images. Existing super-resolution methods based on convolutional neural networks(CNN) can learn the mapping relationship ...
Lin Cao +4 more
doaj +1 more source
SUPER RESOLUTION FOR SINGLE SATELLITE IMAGE USING A GENERATIVE ADVERSARIAL NETWORK [PDF]
ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 2022 Inspired by the immense success of deep neural network in image processing and object recognition, learning-based image super resolution (SR) methods have been highly valued and have become the mainstream direction of super resolution research.
R. Li, W. Liu, W. Gong, X. Zhu, X. Wang
doaj +1 more source
Super-resolution in computational imaging [PDF]
Micron, 2003 Super-resolution is a word used in different contexts but mainly in the case of methods aimed at improving the resolution of an optical instrument beyond the diffraction limit. Such a result may be achieved by means of specific instrumental techniques (such as, for instance, interferometry) or by means of a suitable processing of a digital image; in ...
BERTERO, MARIO, BOCCACCI, PATRIZIA
openaire +3 more sources
Deep Neural Network for Image Super Resolution Driven by Prior Denoising
Nantong Daxue xuebao. Ziran kexue ban, 2021 In order to improve image super resolution, a double layer convolution neural network in image denoising is embedded in image restoration tasks. The image super resolution method driven by prior denoising with deep neural network is proposed.
CHENG Fanqiang;ZHU Yonggui;, ZHU Yonggui
doaj +1 more source
IET Image Processing, 2023 In recent years, terahertz imaging technology has been widely used in security, medicine, and other fields. However, the image resolution is low due to the limits of imaging equipment and diffraction. Traditional super‐resolution methods based on machine
Huanyu Liu, Haipeng Guo, Xiaodong Liu
doaj +1 more source
A Review of GAN-Based Super-Resolution Reconstruction for Optical Remote Sensing Images
Remote Sensing, 2023 High-resolution images have a wide range of applications in image compression, remote sensing, medical imaging, public safety, and other fields. The primary objective of super-resolution reconstruction of images is to reconstruct a given low-resolution ...
Xuan Wang +3 more
doaj +1 more source
Efficient Long-Range Attention Network for Image Super-resolution [PDF]
European Conference on Computer Vision, 2022 Recently, transformer-based methods have demonstrated impressive results in various vision tasks, including image super-resolution (SR), by exploiting the self-attention (SA) for feature extraction.
Xindong Zhang +3 more
semanticscholar +1 more source
Super-Resolution Imaging [PDF]
Journal of Electronic Imaging, 2013 This book serves as an introduction to the flourishing field of super-resolution imaging. It is a compiled volume, with different authors for each of its 14 chapters. While not having a strong outline or textbook format, the chapters group into several sections.
openaire +1 more source
Super-resolution image transfer by a vortex-like metamaterial [PDF]
, 2013 We propose a vortex-like metamaterial device that is capable of transferring image along a spiral route without losing subwavelength information of the image. The super-resolution image can be guided and magnified at the same time with one single design.
Cui, Tie Jun +3 more
core +2 more sources
Deeply-Recursive Convolutional Network for Image Super-Resolution [PDF]
Computer Vision and Pattern Recognition, 2015 We propose an image super-resolution method (SR) using a deeply-recursive convolutional network (DRCN). Our network has a very deep recursive layer (up to 16 recursions).
Jiwon Kim, Jung Kwon Lee, Kyoung Mu Lee
semanticscholar +1 more source