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Image Super-resolution Using Mid-level Representations

Li Yang, Yaxing Wang, Xiaomin Mu, Yaping Wang
2016 DEStech Transactions on Engineering and Technology Research  

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An end-to-end six layers convolutional neural network(CNNs) structure is proposed to realize single image super-resolution reconstruction. According to the study of image Mid-Level Representations(MLR), the network layers are divided into two parts. The first part is used to obtain shallow informations by transferring features, and the second part is used to realize the feature enhancement. The input of the network is low-resolution (LR) image, and the output is the superresolution (SR) image.
more » ... romising experimental results are obtained with higher precision.
doi:10.12783/dtetr/iect2016/3750 fatcat:7aqy4lax5bgj7i5v5b3frv6zsi
Citation

Li Yang, Yaxing Wang, Xiaomin Mu, Yaping Wang. "Image Super-resolution Using Mid-level Representations." DEStech Transactions on Engineering and Technology Research (2016)