Abstract
Recently, Convolutional Neural Network (CNN) based
models have achieved great success in Single Image SuperResolution (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
target image, at the cost of requiring enormous parameters.
This paper proposes a very deep CNN model (up to 52 convolutional layers) named Deep Recursive Residual Network
(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 networks; recursive learning is used to control the model parameters while increasing the depth. Extensive benchmark
evaluation shows that DRRN significantly outperforms state
of the art in SISR, while utilizing far fewer parameters.
Code is available at https://github.com/tyshiwo
/DRRN CVPR17.