Results 21 to 30 of about 390,825 (273)
Quantum Annealing for Single Image Super-Resolution
2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), 2023 Accepted to IEEE/CVF CVPR 2023, NTIRE Challenge and Workshop.
Han Yao Choong +2 more
openaire +3 more sources
Analog Image Modeling for 3D Single Image Super Resolution and Pansharpening
Frontiers in Applied Mathematics and Statistics, 2020 Image super-resolution is an image reconstruction technique which attempts to reconstruct a high resolution image from one or more under-sampled low-resolution images of the same scene.
Richard Lartey +3 more
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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
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Anchored neighborhood deep network for single-image super-resolution
EURASIP Journal on Image and Video Processing, 2018 Real-time image and video processing is a challenging problem in smart surveillance applications. It is necessary to trade off between high frame rate and high resolution to meet the limited bandwidth requirement in many specific applications.
Wuzhen Shi +4 more
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Deep coordinate attention network for single image super‐resolution
IET Image Processing, 2022 Deep learning techniques and deep networks have recently been extensively studied and widely applied to single image super‐resolution (SR). Among them, channel attention has garnered the most focus owing to its significant boost in the presentational ...
Chao Xie, Hongyu Zhu, Yeqi Fei
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Pairwise Operator Learning for Patch Based Single-image Super-resolution [PDF]
, 2016 Motivated by the fact that image patches could be inherently represented by matrices, single-image super-resolution is treated as a problem of learning regression operators in a matrix space in this paper. The regression operators that map low-resolution
Shao, Ling, Tang, Yi
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Applied Sciences, 2020 The super-resolution generative adversarial network (SRGAN) is a seminal work that is capable of generating realistic textures during single image super-resolution. However, the hallucinated details are often accompanied by unpleasant artifacts.
Zetao Jiang, Yongsong Huang, Lirui Hu
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Pyramidal dense attention networks for single image super‐resolution
IET Image Processing, 2022 Recently, residual and dense networks have effectively promoted the development of image super‐resolution (SR). However, most dense networks based SR methods do not make full use of dense feature information.
Huapeng Wu +4 more
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Super Resolution for Noisy Images Using Convolutional Neural Networks
Mathematics, 2022 The images in high resolution contain more useful information than the images in low resolution. Thus, high-resolution digital images are preferred over low-resolution images.
Zaid Bin Mushtaq +5 more
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Volatile-Nonvolatile Memory Network for Progressive Image Super-Resolution
IEEE Access, 2021 Single-image super-resolution, i.e., reconstructing a high-resolution image from a low-resolution image, is a critical concern in many computer vision applications. Recent deep learning-based image super-resolution methods employ massive numbers of model
Jun-Ho Choi +3 more
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