scholarly journals Correction to: Single Image Super-Resolution via a Holistic Attention Network

2020 ◽  
pp. C1-C1
Author(s):  
Ben Niu ◽  
Weilei Wen ◽  
Wenqi Ren ◽  
Xiangde Zhang ◽  
Lianping Yang ◽  
...  
Keyword(s):  
Super Resolution ◽  
Single Image ◽  
Attention Network ◽  
Image Super Resolution ◽  
Single Image Super Resolution

SRGAT: Single Image Super-Resolution with Graph Attention Network

2021 ◽  
pp. 1-1
Author(s):  
Yanyang Yan ◽  
Wenqi Ren ◽  
Xiaobin Hu ◽  
Kun Li ◽  
Haifeng Shen ◽  
...  
Keyword(s):  
Super Resolution ◽  
Single Image ◽  
Attention Network ◽  
Image Super Resolution ◽  
Single Image Super Resolution

scholarly journals Single Image Super-Resolution with Arbitrary Magnification Based on High-Frequency Attention Network

Mathematics ◽  
2022 ◽  
Vol 10 (2) ◽  
pp. 275
Author(s):  
Jun-Seok Yun ◽  
Seok-Bong Yoo
Keyword(s):  
High Frequency ◽  
Satellite Image ◽  
Super Resolution ◽  
Spectral Domain ◽  
Single Image ◽  
Substantial Impact ◽  
Attention Network ◽  
Frequency Components ◽  
Image Super Resolution ◽  
Single Image Super Resolution

Among various developments in the field of computer vision, single image super-resolution of images is one of the most essential tasks. However, compared to the integer magnification model for super-resolution, research on arbitrary magnification has been overlooked. In addition, the importance of single image super-resolution at arbitrary magnification is emphasized for tasks such as object recognition and satellite image magnification. In this study, we propose a model that performs arbitrary magnification while retaining the advantages of integer magnification. The proposed model extends the integer magnification image to the target magnification in the discrete cosine transform (DCT) spectral domain. The broadening of the DCT spectral domain results in a lack of high-frequency components. To solve this problem, we propose a high-frequency attention network for arbitrary magnification so that high-frequency information can be restored. In addition, only high-frequency components are extracted from the image with a mask generated by a hyperparameter in the DCT domain. Therefore, the high-frequency components that have a substantial impact on image quality are recovered by this procedure. The proposed framework achieves the performance of an integer magnification and correctly retrieves the high-frequency components lost between the arbitrary magnifications. We experimentally validated our model’s superiority over state-of-the-art models.

scholarly journals A comprehensive review of deep learning-based single image super-resolution

2021 ◽  
Vol 7 ◽  
pp. e621
Author(s):  
Syed Muhammad Arsalan Bashir ◽  
Yi Wang ◽  
Mahrukh Khan ◽  
Yilong Niu
Keyword(s):  
Deep Learning ◽  
Super Resolution ◽  
Single Image ◽  
Open Problems ◽  
Future Directions ◽  
Attention Network ◽  
Domain Specific ◽  
Feedback Network ◽  
Image Super Resolution ◽  
Single Image Super Resolution

Image super-resolution (SR) is one of the vital image processing methods that improve the resolution of an image in the field of computer vision. In the last two decades, significant progress has been made in the field of super-resolution, especially by utilizing deep learning methods. This survey is an effort to provide a detailed survey of recent progress in single-image super-resolution in the perspective of deep learning while also informing about the initial classical methods used for image super-resolution. The survey classifies the image SR methods into four categories, i.e., classical methods, supervised learning-based methods, unsupervised learning-based methods, and domain-specific SR methods. We also introduce the problem of SR to provide intuition about image quality metrics, available reference datasets, and SR challenges. Deep learning-based approaches of SR are evaluated using a reference dataset. Some of the reviewed state-of-the-art image SR methods include the enhanced deep SR network (EDSR), cycle-in-cycle GAN (CinCGAN), multiscale residual network (MSRN), meta residual dense network (Meta-RDN), recurrent back-projection network (RBPN), second-order attention network (SAN), SR feedback network (SRFBN) and the wavelet-based residual attention network (WRAN). Finally, this survey is concluded with future directions and trends in SR and open problems in SR to be addressed by the researchers.

scholarly journals HRAN: Hybrid Residual Attention Network for Single Image Super-Resolution

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 137020-137029 ◽  
Author(s):  
Abdul Muqeet ◽  
Md Tauhid Bin Iqbal ◽  
Sung-Ho Bae
Keyword(s):  
Super Resolution ◽  
Single Image ◽  
Attention Network ◽  
Image Super Resolution ◽  
Single Image Super Resolution

scholarly journals Mixed High-Order Non-local Attention Network For Single Image Super-resolution

IEEE Access ◽  
2021 ◽  
pp. 1-1
Author(s):  
Xiaobiao Du ◽  
Saibiao Jiang ◽  
Yujuan Si ◽  
Lina Xu ◽  
Chongjin Liu
Keyword(s):  
Super Resolution ◽  
High Order ◽  
Single Image ◽  
Attention Network ◽  
Non Local ◽  
Image Super Resolution ◽  
Single Image Super Resolution

scholarly journals Interpretable Detail-Fidelity Attention Network for Single Image Super-Resolution

2021 ◽  
Vol 30 ◽  
pp. 2325-2339
Author(s):  
Yuanfei Huang ◽  
Jie Li ◽  
Xinbo Gao ◽  
Yanting Hu ◽  
Wen Lu
Keyword(s):  
Super Resolution ◽  
Single Image ◽  
Attention Network ◽  
Image Super Resolution ◽  
Single Image Super Resolution
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