Convolutional Neural Network Based Models for Improving Super-Resolution Imaging

Many fields, such as remote sensing, medical imaging, and biological detection, pose a technical challenge for achieving super-resolution imaging. Convolutional neural networks (CNNs) are considered one of the potential solutions to realize the super-resolution. In this paper three-layer, CNN-based...

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Bibliographic Details
Published in:IEEE access 2019, Vol.7, p.43042-43051
Main Authors: Sun, Yingyi, Zhang, Wei, Gu, Hao, Liu, Chao, Hong, Sheng, Xu, Wenhua, Yang, Jie, Gui, Guan
Format: Article
Language:English
Subjects:
Adaptive algorithms
adaptive moment estimation
Artificial neural networks
Biomedical imaging
Convolution
convolutional neural networks
deep learning
Image reconstruction
Image resolution
Medical imaging
Neural networks
Optimization
Remote sensing
Signal resolution
Super-resolution imaging
Training
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Summary:Many fields, such as remote sensing, medical imaging, and biological detection, pose a technical challenge for achieving super-resolution imaging. Convolutional neural networks (CNNs) are considered one of the potential solutions to realize the super-resolution. In this paper three-layer, CNN-based models are proposed to reconstruct the super-resolution images using four optimization algorithms, i.e., stochastic gradient descent, adaptive gradient (AdaGrad), root mean square prop (RMSprop), and adaptive moment estimation (ADAM). Among these four optimizations, ADAM is considered to have the best performance. To further verify the impact of the number of convolution layers on performance, a selection of CNN-based models with four convolutional layers is then proposed, each of which is named with the convolution parameters. All the four-layer models are optimized with ADAM, and the experimental results indicate that the 9-3-3-5 model achieves the best performance in the super-resolution reconstruction task.
ISSN:2169-3536
2169-3536
DOI:10.1109/ACCESS.2019.2908501