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Survey of single image super‐resolution reconstruction
IET Image Processing, 2020 Image super‐resolution reconstruction refers to a technique of recovering a high‐resolution (HR) image (or multiple images) from a low‐resolution (LR) degraded image (or multiple images).
Kai Li +4 more
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Single image super-resolution with denoising diffusion GANS [PDF]
Scientific ReportsSingle image super-resolution (SISR) refers to the reconstruction from the corresponding low-resolution (LR) image input to a high-resolution (HR) image. However, since a single low-resolution image corresponds to multiple high-resolution images, this is
Heng Xiao +6 more
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Review of Image Super-resolution Reconstruction Algorithms Based on Deep Learning [PDF]
Jisuanji kexue yu tansuo, 2022 The essence of image super-resolution reconstruction technology is to break through the limitation of hardware conditions, and reconstruct a high-resolution image from a low-resolution image which contains less infor-mation through the image super ...
YANG Caidong, LI Chengyang, LI Zhongbo, XIE Yongqiang, SUN Fangwei, QI Jin
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Image Super-Resolution Reconstruction Method Based on Embeddable Network Structure [PDF]
Jisuanji gongcheng, 2021 The existing Super-Resolution(SR) image reconstruction models based on Convolutional Neural Networks(CNN) have multiple deficiencies,such as instable model training process and low convergence speed.To address the problem,this paper proposes an ...
QIANG Baohua, PANG Yuanchao, YANG Minghao, ZENG Kun, ZHENG Hong, XIE Wu, MO Ye
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Learning Curvelet-based Directional Dictionaries for Single Image Super Resolution [PDF]
AUT Journal of Electrical Engineering, 2021 Learning and reconstruction-based methods are the two main approaches to the solve single image super resolution (SISR) problem. In this paper, to exploit the advantages of both learning based and reconstruction based approaches, we propose a new SISR ...
Elhameh Mikaeli +2 more
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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
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Super-resolution microscopy based on interpolation and wide spectrum de-noising
Компьютерная оптика, 2023 In the conventional single-molecule localizations and super-resolution microscopy, the pixel size of a raw image is approximately equal to the standard deviation of the point spread function.
T. Cheng, T. Chenchen
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Cross-View Attention Interaction Fusion Algorithm for Stereo Super-Resolution
Applied Sciences, 2023 In the process of stereo super-resolution reconstruction, in addition to the richness of the extracted feature information directly affecting the texture details of the reconstructed image, the texture details of the corresponding pixels between stereo ...
Yaru Zhang +3 more
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Single Image Super Resolution via Multi-Attention Fusion Recurrent Network
IEEE Access, 2023 Deep convolutional neural networks have significantly enhanced the performance of single image super-resolution in recent years. However, the majority of the proposed networks are single-channel, making it challenging to fully exploit the advantages of ...
Qiqi Kou +5 more
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Review and Prospect of Image Super-Resolution Technology
Jisuanji kexue yu tansuo, 2020 Image super-resolution (SR) is an important type of image processing technology for improving image and video resolution in computer vision. In recent years, thanks to the success of neural networks, image super-resolution technology based on deep ...
LIU Ying, ZHU Li, LIM Kengpang, LI Yinghua, WANG Fuping, LU Jin
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