Image Super-Resolution Based on Variational Autoencoder and Channel Attention
Abstract
References
Recommendations
Attention-Aware Linear Depthwise Convolution for Single Image Super-Resolution
SMA 2020: The 9th International Conference on Smart Media and ApplicationsAlthough deep convolutional neural networks (CNNs) have obtained outstanding performance in image super-resolution (SR), their computational cost increases geometrically as CNN models get deeper and wider. Meanwhile, the features of intermediate layers ...
Deep Residual Attention Network for Spectral Image Super-Resolution
Computer Vision – ECCV 2018 WorkshopsAbstractSpectral imaging sensors often suffer from low spatial resolution, as there exists an essential tradeoff between the spectral and spatial resolutions that can be simultaneously achieved, especially when the temporal resolution needs to be ...
Reference Image Guided Super-Resolution via Progressive Channel Attention Networks
AbstractIn recent years, the convolutional neural networks (CNNs) for single image super-resolution (SISR) are becoming more and more complex, and it is more challenging to improve the SISR performance. In contrast, the reference image guided super-...
Comments
Information & Contributors
Information
Published In
Publisher
Association for Computing Machinery
New York, NY, United States
Publication History
Check for updates
Author Tags
Qualifiers
- Research-article
- Research
- Refereed limited
Conference
Contributors
Other Metrics
Bibliometrics & Citations
Bibliometrics
Article Metrics
- 0Total Citations
- 21Total Downloads
- Downloads (Last 12 months)21
- Downloads (Last 6 weeks)4
Other Metrics
Citations
Cited By
View allView Options
Login options
Check if you have access through your login credentials or your institution to get full access on this article.
Sign inFull Access
View options
View or Download as a PDF file.
PDFeReader
View online with eReader.
eReaderHTML Format
View this article in HTML Format.
HTML Format