Towards realistic image via function learning

There has been a remarkable growth in computer vision due to the introduction of deep convolutional neural network . In most electronic imaging applications, images with high resolution are desired and cannot be ignored in many crucial applications. Super-resolution is a technique that enhances the resolution of images from the low-resolution input. Even thought, the performance of pattern recognition in computer vision will be improved if high resolution image is provided. The current super-resolution models based convolutional neural network has shown great performance, and also could outpace the other models. Depth in of CNN models is crucial importance for image super-resolution. However, the deeper networks based SR techniques are more difficult to train. To address these problems we propose a very deep residual network which comprises residual in residual structure to form a very deep network. In particular, the proposed model consists of several residual units with long skip connection. The proposed model allows low-frequency information to be bypassed through multiple skip connections, and the high-frequency information will be centralized in the main network. Extensive experiments show that our proposed model achieves better performance against state-of-the-art methods.