Transcription of Image Super-Resolution via Deep Recursive Residual Network
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Image Super-Resolution via Deep Recursive Residual Network
Image Super-Resolution via Deep Recursive Residual Network Ying Tai 1 , Jian Yang1 , and Xiaoming Liu2. 1. Department of Computer Science and Engineering, Nanjing University of Science and Technology 2. Department of Computer Science and Engineering, Michigan State University {taiying, Abstract DRRN_B1U25. DRRN_B1U9. Recently, Convolutional Neural Network (CNN) based DRCN RED30. models have achieved great success in single Image super - VDSR. Resolution (SISR). Owing to the strength of deep networks, these CNN models learn an effective nonlinear mapping from the low-resolution input Image to the high-resolution CSCN. target Image , at the cost of requiring enormous parameters. ESPCN. This paper proposes a very deep CNN model (up to 52 con- volutional layers) named Deep Recursive Residual Network SRCNN. (DRRN) that strives for deep yet concise networks. Specifi- cally, Residual learning is adopted, both in global and local manners, to mitigate the difficulty of training very deep net- Figure 1.}
Single Image Super-Resolution (SISR) is a classic com-puter vision problem, which aims to recover a high-resolution (HR) image from a low-resolution (LR) image. Since SISR restores the high-frequency information, it is widely used in applications such as medical imaging [26], satellite imaging [29], security and surveillance [37], where
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