Deep Networks for Single Image Super-Resolution with Multi-context Fusion

Deep convolutional neural networks have been successfully applied to image super resolution. In this paper, we propose a multi-context fusion learning based super resolution model to exploit context information on both smaller image regions and larger image regions for SR. To speed up execution time, our method directly takes the low-resolution image (not interpolation version) as input on both training and testing processes and combines the residual network at the same time. The proposed model is extensively evaluated and compared with the state-of-the-art SR methods and experimental results demonstrate its performance in speed and accuracy.