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Analysis of Single Image Super Resolution Models
2022 International Conference on Electrical, Computer, Communications and Mechatronics Engineering (ICECCME), 2022 Image Super-Resolution (SR) is a set of image processing techniques which improve the resolution of images and videos. Deep learning approaches have made remarkable improvement in image super-resolution in recent years. This article aims and seeks to provide a comprehensive analysis on recent advances of models which has been used in image ...
Köprülü, Mertali +1 more
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Suitability of Single Image Super Resolution Models for Video Super Resolution
International Journal of Engineering and Advanced Technology, 2020 This project is an attempt to understand the suitability of the Single image super resolution models to video super resolution. Super Resolution refers to the process of enhancing the quality of low resolution images and video. Single image super resolution algorithms refer to those algorithms that can be applied on a single image to enhance its ...
Shreyas D G +2 more
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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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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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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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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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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
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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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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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